health equity, the key for transformational change

AN ATLAS THROUGH GRAPHS AND MAPS REVEALING THE NEED FOR AN INTERNATIONAL, NATIONAL AND SOCIAL CHANGE TO ENABLE THE

UNIVERSAL RIGHT TO HEALTH

JUAN GARAY

NEFER KELLEY Y DAVID CHIRIBOGACOLLABORATORS

HEALTH EQUITYTHE KEY FOR TRANSFORMATIONAL CHANGE

Editing by: Editorial Nacional de Salud y Seguridad Social (EDNASSS). 2015 Centro de Desarrollo Estratégico e Información en Salud y Seguridad Social (CENDEISSS) Caja Costarricense de Seguro Social Tel.: (506)2221-6193 Ext. 217 Arts and impression: Litografía e imprenta Lil S.A. All rights reserved. This publication was authorized on May 04th, 2015, by Juan Garay, intellectual author.

EDNASSS: an editorial at service of health and social security

WA525 G212e Garay Amores, Juan Equidad en salud, la llave para un cambio transformacional = Health equity, the key for transformational change / Juan Garay Amores. – - San José, C. R.: EDNASSS-CCSS, 2015. 260 p.; il.; 21 x 27 cm. ISBN: 978-9968-916-55-4

1. EQUIDAD EN SALUD. 2. ESTADÍSTICAS DE SALUD. 3. SALUD GLOBAL. I. Título

©

Prologue by Dra. María del Rocío Saénz Madrigal, Executive President, Caja Costarricense de Seguro Social, San José, Costa Rica.

This image is a work made by my father, Enrique Garay. It represents the multiple, perhaps endless, equity curves that coexist in the universe. The distortion that the human species suffers from extreme hoarding of their resources and depletion of nature that

sustains it, all this has a tragic price in avoidable deaths. But mostly, it is a tribute to my father and his constant inspiration and support in my life. Juan Garay.

To Nefer, my life companion in all,

and constant inspiration. To my parents, who always encouraged me

to follow my dreams.

México City, 2015.

Health equity, the key for transformational change

TABLE OF CONTENTS

About the author .................... .. . . ...

7

Prologue .

9

Foreword

11

I. Summary. .. . ..

13

II. Introduction to the challenge of health equity. . .

14

III. Rescuing and updating the ethical principles of health in XXIst Century. . . Updating the World Health Organization definition of health. ...... Recognition and operationalization of the right to health: State, international, social and individual responsibilities. .. .. ...... Setting principles of global health. . .. .

16 16 17 18

IV. Equity vs inequality. . The neutral statistical approach .. . Main health influencing variables .. Inequalities across disaggregating variables ... Inequality vs inequity. . . The relevance of estimating the burden of health inequity. .............

Burden of health inequity by best standards of influencing variables .. Burden of inequity by thresholds of feasibility and sustainability ..

21 21 26 29 31 32 32 36

V. The burden of global health inequity. .. .

Burden of inequity in loss of life expectancy Burden of global health inequity in avoidable deaths .

Number of avoidable deaths due to global health inequity . . . Proportion of deaths which are avoidable ... . Burden of global health inequity by life years lost .

Distribution of avoidable life years lost by age groups, sex and time.. . ... Demographic pyramid deviations due to global health inequity. ..

44 44 45 46 49 54 55 56

VI. Economic equity to enable global health equity and the universal right to health. . .. World´s national average GDP pc distribution ..... .. . Minimum dignity threshold .. Equity (fair inequality) thresholds and ethical redistribution of GDP

Distribution of the world´s population in relation to the equity thresholds ... Distribution of the world´s GDP in relation to the equity thresholds .. . .... . World´s population and GDP in the "equity zone" . . Ethical GDP redistribution required to enable global health equity .. Redistribution from excess (hoarding) countries to deficit (below mDTh threshold) countries. Redistribution levels if lower limits of HFS models are taken as minimum dignity threshold....... Comparative analysis of ODA with redistribution required for global health equity......................

57 57 61 61 63 66 70 71 74 76 79

5

VII. National health equity . . .. .

Avoidable deaths by global health equity, due to national inefficiency/inequity . Life expectancy adjusted to inequality .. Health equity monitor . . ........... Framework for international reporting on national health equity ..

82

82 83 84 86

VIII. Holistic health index . .

Methodology . . Estimates of hoarding and exhausting negative impact on others health .. ... Relation between hoarding and exhausting and the questioning of economic growth beyond the hoarding threshold .. ... .. . Dimensions of the holistic health index . .. . .... Holistic health index .

87

87 87

88 90 93

IX. Conclusions and political proposals .. .

Conclusions ..Proposals ..Research agenda . .. .

International level. National level.. .. Community and individual levels .. . . ...

96

96 97 98 98 98 99

X. Annexes ...

Annex 1: Burden of global health inequity in loss of life expectancy ... Annex 2: Burden of global health inequity in loss of life expectancy ... ........ ... Annex 3: Burden of global health inequity in proportion of deaths which are avoidable by GHE .. .. ... Annex 4: Deficit or excess (-ve) GDP by global health equity economic thresholds Annex 5: Holistic health index 2012 ... .. Annex 6: List of tables.. . . Annex 7: List of figures Annex 8: List of acronyms ..

100

100 105

110 115 120 123 124 127

6 Health equity, the key for transformational change


ABOUT THE AUTHOR

Juan Garay is a medical doctor from Spain, specialized in internal medicine, infectious diseases and public health. Since the 80s, he has combined clinical, public health, research and design on health systems (as some of the early AIDS treatment programs in Africa), in many countries in Europe, Africa and America, with governments, universities and non-state actors. In 2002 he joined the European Union, working initially in the design of EU health cooperation policies and programs. He gradually explored the roots of health in the areas of human rights -coordinating the work towards the EU external policy on children rights- and the multisectorial dimensions of health which led to his coordination in the development of the EU policy on global health. In relation to global health challenges, he has focused on research on global health equity in the last years, now reflected in this book. Since 2013 he works as head of EU cooperation in Mexico, with special emphasis on analysis and policy dialogue on social cohesion. In parallel to his responsibilities in the EU, Juan Garay is professor of global health in Granada and Madrid, advisor to the South American Institute of Health Governance, visiting lecturer in UC Berkeley, as well as guest lecturer in several universities in America and Europe, where he continues research and academic activities in relation to health and social equity metrics. His passion for justice and health, within and between generations, is reflected in a novel which mixes his life experiences with his vision of the future (http://www.bubok.es/libros/209095/Valentia-y-Ternura-Todos-somos-la-misma-energia).


PROLOGUE

Regarding the health of our populations, there converge multiple social organizations, and academic and labor sectors that debate and coordinate over a minimum basis of consensus, something that in recent years we have considered sacred and immutable: the concept of health, its enjoyment as a fundamental right, and its relevance as a part of sustainable development. Starting from this common base, with somewhat less consensus, we attempt to define the organized response of society to this collective aspiration of health. The role of the individual, the State, the private sector, and intergenerational responsibility have a wider range of conceptual, ideological, and cultural approaches; so, depending on how these factors come together, we also find a greater variance in performance grades and justice of health systems. The exponential speed of change as the main feature of our times, coupled with the inability we have had to ensure optimal conditions for everyone, ironically, could have led us to an acceptance of such basic concepts as "universal and timeless" or even -to those who often reject critical and creative thinking in favor of the status quo- as an "unattainable aspiration", or, in other words, as unquestionable ideals to be supported by all ages and generations. However, those same justifications demand that we pause to deconstruct and review the foundation of our actions. We must do it in such a way that we can understand if the broad marginal gaps of the performance of our health systems originate from those paradigms that have governed modern health management, and, therefore, could now contribute to their reduction by way of a revised and renewed ethic, and from the challenges posed by this historical moment. This exercise, deep and scientific as it may be, cannot ignore that there is a clear social demand for the pragmatic implications of every effort we make in the construction and management of health. In this sense, the debate on health equity (with its intrinsic framework of social justice) and sustainability in health, and various complementary frameworks for action such as global health, must be a path to produce viable policies, swift actions, useful tools, and concrete and measurable health impacts of current and future populations from different areas of health management (the individual, the local service networks, health systems, and the global community). The work of Dr. Garay serves three main purposes in this roadmap. The first is to introduce the renewed debate over our paradigms or basic concepts mentioned above: health, the right to health and sustainable development. These should all be seen with a critical view of our new century and assuming that it is an ethical responsibility to adapt our conception of the world into a vision focused on the health needs of the people, understanding needs as a concept that reconciles the biases of supply and demand in health through evidence and real participation. The second purpose, addressed with an important proactive approach, is to propose a concrete measurement of the discourse of (in) equity and the dimension -as yet ethereal- of the best achievable status of health. Concepts such as the burden of inequity or the holistic health index make way for the new generation of metrics to address the analysis of health systems, the management of public policies, and their performance models, and move them toward more humanistic and sustainable approaches.

This idea, if it is good for any health system, is mainly valuable for middle- and low-income countries. These two groups of countries urgently deserve strategies differentiated and tailored to the availability of resources to organize their health systems, and a guarantee for their people of the best possible performance in health status, without falling back on the old idea of "unsustainable spending focused on disease care".


The third purpose, interdependent of its predecessors, is to place a large amount of "responsible hope" on the basis of scientific evidence and the collective potential of humanity. In this ambitious undertaking, the analysis developed by Dr. Garay presents an ontological question about the ethical and socio-economic implications for our universal model of development that is usually avoided: "... there is a minimum level of per capita income (minimum threshold of dignity) below which the dignity of life is not respected ". That approach does not lead to the conclusion (conviction) that only the growth of per capita income, per se, implies an improvement of those living conditions. Instead, it reinforces the bidirectional relationship confirmed already by previous analyses: sufficient resources are required to improve living conditions (and, in turn, health), and good living conditions are sine qua non conditions for income growth. Beyond that premise, Dr. Garay posed the existence of Equitable-Sustainable Health models (ESH), that optimize health spending (investment), for the best possible performance in health. And this is the best news that we can provide to middle- and low-income countries in our century: the health of our populations owes a lot to models, approaches, and management styles that policymakers and practitioners of health implement in our environments. This grand conclusion ultimately presents us with a great challenge, which Costa Rica, along with other developing countries, has come to sponsor in the global context: there is no international framework for monitoring levels and dynamics of health (in) equity, but we are still making decisions based on a framework of inequalities in health, both locally and globally. Is here where knowledge becomes an ethical demand to human beings and, in particular, those who have the ability and access to study this work: if there is a best possible health status for our generation, which, in turn, does not compromise the generations to come, why are we waiting to bring the desired and possible future of health-however many struggles we may face- to the present? The time for action is now! Dra. María del Rocío Sáenz Madrigal Executive President Caja Costarricense de Seguro Social San Jose, Costa Rica July, 2015


FOREWORD

Have you ever wondered why is it that the World Health Organization’s definition of health as the optimal level of physical and psychological wellbeing and not just the absence of infirmity is such a vague statement? How this lack of precision in the definition prevents us from having a clear-cut overarching and measurable global health goal? And, how this ambiguity allows global health inequities to continue growing under the radar, while governments are not accountable to their constituents or to any international body regarding the universal right to health of all people? “HEALTH EQUITY, THE KEY FOR TRANSFORMATIONAL CHANGE” proposes an urgent need to have a new and practical definition of the best attainable health for all that will allow us to express a concise, ethical and universal goal, incorporating individual and collective dimensions to objectively measure the health equity gap between and within countries. The book not only uncovers a stark reality, but also renders a grim picture of global health inequity with comprehensive documentation, highlighting a long-standing and ongoing worldwide death toll of about 50,000 avoidable deaths every day, which is ethically intolerable and can only be explained by economic injustice. With a deep understanding of the issues at play drawn from his extensive experience working as a primary healthcare physician in Zimbabwe and from his lead in the EU Policy on Global Health, Dr. Juan Garay identifies a clear global health objective that takes into consideration not only viable, but ecological targets, developing a healthy, feasible and sustainable (HFS) model, through a novel approach, utilizing readily available data and identifying specific countries which fit the criteria of the model. The analyses also identify countries and population subgroups mostly affected by inequity. Dr. Garay shows important conclusions from these analyses and articulates practical insights to eliminate global health inequities with extensive information with time trends from the 1960’s to the present, organized in detailed tables, figures and maps. After quantifying the cost of bridging the global health equity gap, he manages to outline a mechanism to finance the necessary interventions through a principled-approach of a binding global redistribution system; he compares such a methodology with the outdated, arbitrary and inefficient international cooperation model. The approach considers global levels of poverty and excessive global accumulation, which abuses natural resources in such a way as to deprive current and future generations from the access they deserve, while contaminating the planet beyond its capacity, alluding to the concept of intergenerational equity. The book discusses crucial concepts like the difference between equity and equality, the global burden of health equity, the minimum income threshold for dignity, the maximum threshold of income above which excessive accumulation or hoarding occurs, and how resource hoarding is directly linked to the burden of health equity; while also proposing a holistic health index, including healthy life expectancy by gender, the happiness index, and life-years lost of others due to the hoarding effect and to exhausting effect. From my perspective as a physician working for many years with underserved indigenous communities in Ecuador, and from a public health point of view, as former Minister of Health of Ecuador and former President of the Council of Health Ministers of South America, this exceptional volume provides an alternative and practical approach to identifying and quantifying gaps in health equity. The methodology provides tools to defend the right to health for all by supporting the development of binding instruments linked to concrete health standards attainable through a financially sustainable mechanism. As such it is an important contribution to the efforts of healthcare workers, public health officials and scholars, in addition to those of community and political leaders at the local, national, regional and global levels. This remarkable book will transform your perspective on global public health, and will motivate you to join the fight for health equity, one of the most important challenges of our generation. David Chiriboga, MD, MPH Zumbahua, Ecuador Former Minister of Health of Ecuador Former President of the Council of Health Ministers of South America


Juan Garay has written a book that is of the highest value to the pursuit of global health justice. He shines a light on the glaring and unconscionable health inequalities that exist in rich and poor countries alike. With new methodology and meticulous precision, Juan estimates for the first time that there are some 17 million annual deaths each year that are needless according to healthy, equitable and sustainable models. The world needs to reflect on how to move people and resources to the “equity zone", compatible with the universal right to health. Professor Lawrence O. Gostin Founding and Faculty Director - O'Neill Chair in Global Health Law Director, WHO Collaborating Center on Public Health Law & Human Rights Georgetown Law School, Washington, USA This is a book which aims at something very few dare to do: establish a relation between ethics and quantitative probability sciences. On the one side, equity, the right to health, dignity and the social responsibility of the State; on the other, the measurement of avoidable mortality and the burden of global health inequity, the estimates of the effects of hoarding and exhausting resources, besides the proposal of an holistic health index. The interpretation of these relations requires a multidisciplinary approach, another interesting dimension of this manuscript. Finally, the book´s reading suggests a debate on the importance of the access to quality care and the efficiency of the provision of health services, two health determinants that while the book does not directly deal with, will allow discussion on the classifications hereby presented and benefit the analysis based on the parameters explored by Juan Garay in his innovating essay. Professor Jean Pierre Unger Public Health Department Eminent Scientist of the Year 2004 International Award Institute of Tropical Medicine- Antwerp- Belgium


I. SUMMARY

Before reading this work, be aware that it challenges many of the present global concepts and policies on health. It dares to do that based on the evidence of the tragic death toll from global health inequity (injustice), which requires a deep transformation of concepts and dynamics towards the universal right to health. This work challenges the definition of health (to include equity and sustainability dimensions), the lack of accountability in the guarantee of the right to health (so that the ICECSRs and its optional protocol not be ignored), the international denial of measuring health equity (and instead describing inequalities), the strategies of measuring inequalities-acting on poverty (and the need to shift to measure inequity and ensuring minimum dignity for all), the present international cooperation framework (versus a binding redistribution mechanism to enable the right to health) and the human development indicators (proposing a holistic health index which incorporates individual and also collective dimensions of our effects on others). Health is a universal human right, as recognized in the Universal Declaration of Human Rights (UDHR). Health equity -best feasible health for all- is the main principle of global health accepted by all countries, as recognized in the founding objective of the World Health Organization. However, as the levels of best feasible health have never been agreed upon, the state and trend of health equity and inversely the burden of health inequity have never been measured. Daring to set best feasible health standards for all, across and within countries, is essential to operationalize and measure the universal right to health. We hereby propose a very simple, solid and understandable (and sustainable, to expand the feasibility across generations) method to set best feasible health standards: we define countries that for the last 70 years have complied with three criteria (UN available demographic statistics): 1) life expectancy above the world´s weighted average (by countries population sizes), 2) GDP pc below the world´s weighted average (feasibility) and 3) carbon footprint below the planetary boundary (sustainability). This method can be applied at subnational and regional levels. Only fourteen countries have constantly met the mentioned criteria. We analyzed their average health indicators disaggregated by age periods, sex and across time, and enable the calculation of the burden of health inequity by countries, ages, sex and time periods. Such analysis is represented in charts and maps which are only a small sample of the interactive database of maps and graphs of global health equity. This analysis concludes that over one in three annual deaths worldwide are avoidable by global health equity. In the last five year period, the annual average of avoidable deaths was over 17 million deaths, 2000 every hour. Most of the avoidable deaths took place in the countries with GDP pc below the minimum income threshold defined by the healthy-feasible-sustainable (HFS) models. This tragic death toll due to global injustice challenges the global economic and cooperation framework. The redistribution required to enable all persons in the world to have the chance to enjoy a globally feasible-sustainable minimum level of life expectancy challenges the far lower, inequitable and volatile levels of development "aid". The work ends by challenging also the global rating of development (as the Human Development Index), by introducing the dimensions of our effects on others (here or to come), through the effects of hoarding and/or exhausting natural and economic resources.


II. INTRODUCTION TO THE CHALLENGE OF HEALTH EQUITY

Health is -even by the limited physical dimensions we often measure it: a lengthy life deprived of disability-, one of the main aspirations of human beings, across times in history, across cultures and religions. It is one of the main forces driving research and innovation, economic activity, and political attention. The term global health has replaced the international health definition, incorporating the complex variety and interaction of health actors and factors. The main principles of global health -at international or local level- may be summarized in that health should be by all people -Alma Ata-, in all policies -Ottawa- and for all persons -universal health coverage. The main guarantee towards such principles is a legal framework that recognizes the right to health. At country level, over 100 national constitutions recognize the right to health of all their citizens and most national health policies have such aim as their main objective. All countries declared the recognition of the right to health in article 25 of the Universal Declaration of Human Rights, but very few countries are held accountable to an international committee where their citizens may report to if governments fail to comply. Beyond national constitutions and international treaties, how can the status of the universal right to health be assessed? Some estimate that over one third of people in the world have no access to health services. But even those with access to health services may be deprived of their right to health as living conditions deter their health and even the best health care does not recover more than one third of health lost to other causes. On the other side, health disparities may be due to people (genetic), places (epidemiology) or even the culture (religion, traditions), contexts difficult to change within a generation. This challenge is phrased in the only common health objective shared by all countries: article 1 of the constitutional charter of the World Health Organization (since 1945): to achieve the best feasible standard of Health by all peoples1. Health differences are regularly measured and reported within and between countries. The world health statistics report every year a wide array of countries’ average health indicators which picture health inequalities between countries. The Commission on Social Determinants for Health reported on health inequalities between and within countries and the factors (almost every political, social, economic and cultural factor) associated with such disparities. The report’s recommendations were welcomed by the World Health Assembly in 2010, and based on this report, they agree to call the members countries to report on “health equity”. However, only one in three countries is recorded under WHO’s “Health equity monitor” on the differences or ratios (inequalities) of health and health services’ indicators between populations of different places (urban/rural), education (mother’s education) or income (by income quintiles). Only low and low-middle income countries are included through the analysis of surveys2. Even those reports fail to estimate “health equity”. There is a fundamental difference between inequality and inequity. Inequality measures the mathematical differences of a variable between two individuals or groups featured by another influencing variable. In contrast, inequity measures the fair level of inequality. It recognizes a range of differences due to factors not amenable to change (as genetic and epidemiologic) and the threshold beyond which differences are unfair and avoidable. It defines, therefore, the beast feasible levels of health and how it reaches -or doesn’t (burden of inequity)- reach all people. But, so far, the burden of health inequity is not regularly monitored or reported in most countries. 1 http://www.who.int/governance/eb/who_constitution_en.pdf 2 Demographic Household Surveys (DHS) and Multiple Indicator Cluster Survey (MICS).


g y ( ) y ( )Being health one of the main individual and collective aspirations, recognized in national and international declarations (yet with failed accountability) and the common health goal of all nations, why has the level of its common global objective -the best feasible health - never been estimated, and hence neither how that level reaches all peoples? Health is a consequence (and often an interactive cause) of almost every policy area. Therefore, health inequity may be the best indicator of social cohesion and social justice. There is an additional dimension of equity which needs to be considered 70 years after the shared international health goal, and, in fact, to every national or international policy objective: sustainability. The abuse of natural resources today has an impact in the health of coming generations, that is, on inter-generational health equity. This was well recognized by the Lancet commission on Global Governance for Health: that the global economic system should serve a global population of healthy people in sustainable societies, within the boundaries of nature3.

3 Ottersen OP, Dasgupta J, Blouin C, Buss P, Chongsuvivatwong V, Frenk J, et al. The political origins of health inequity: prospects for change. Lancet (Internet) 2014; 383(9917): 630-667. Available from: http://www.globalweek.gu.se/

Health equity may be the most sensitive indicator of social cohesion -social justice- and

contributes to modulate the root causes of health, shifting from a mitigating to a transformational role.


III. RESCUING AND UPDATING THE ETHICAL PRINCIPLES OF HEALTH IN XXIST CENTURY

UPDATING THE WORLD HEALTH ORGANIZATION DEFINITION OF HEALTH At the time of the foundation of the World Health Organization, health was defined as “a state of complete physical, social and mental well-being, and not merely the absence of disease or infirmity”4. Such definition has been widely accepted and repeatedly quoted in the last 60 years, but the psychological and social wellbeing dimensions have not been defined and no standard indicators have been agreed upon. These dimensions are not captured in the world health statistics or in any of the targets agreed in the thousands of world health resolutions or international agreements. Despite the global neglect this definition has been subject to, it also requires, in our opinion, an update or review of: a. Firstly, the “complete” wellbeing may seldom be felt by most people, and rather we all adjust to

some acute or chronic health challenges and handicaps so as to enjoy life personally and our relation with society and nature. For instance, wearing glasses does not necessarily mean a limitation to our well-being but an adequate adjustment to a physical challenge.

b. Secondly, our wellbeing should not be -by accumulating limited resources- at the expense of others’

health, or based on the destruction of nature -at the expense of the health of next generations-. Individual health should therefore be qualified by its equity and sustainability.

We hence propose an update in the definition of health linked with the global health objective, which would read:

According to this health definition hereby proposed, individual health should also relate to the responsibility on the health of others (in our generation and those to come). This concept calls for new ways of measuring health beyond its individual enjoyment, often the gravitational centre of the western monotheist religious and philosophical references. At an individual level, we could measure our health not just in healthy life years, but also including the dimensions of social and mental wellbeing (as with the happiness index5). At a collective level, we need to consider our effects on the health of others in our generation through the “hoarding effect” (surpassing the maximum threshold of resources which prevents health equity) and of future generations through the “exhausting effect” (living above the maximum ethical limit of hectare use and carbon footprint below which we may preserve nature -and a healthy life- for coming generations).

4 http://www.who.int/about/definition/en/print.html

“Health is a state of wellbeing through the adjustment to physical, social and mental

challenges in an equitable and sustainable way, which enables the attainment of best feasible standards of health by all peoples”.

5 http://unsdsn.org/wp-content/uploads/2014/02/WorldHappinessReport2013_online.pdf


Such consolidate health index would inform people and communities on the balance between their enjoyment of health and their effects on others and the need to maximize and balance both. As the following sections will show, the measurement of the burden of health equity will enable the assessment of individual and collective negative impacts on the health of others though the mentioned hoarding and exhausting effects. RECOGNITION AND OPERATIONALIZATION OF THE RIGHT TO HEALTH: STATE, INTERNATIONAL, SOCIAL AND INDIVIDUAL RESPONSIBILITIES

At present, health is a human right for some and a commodity or optional benefit for others. This is what differentiates the understanding of the roles of the state and civil society, the legal frameworks and the market regulations, and the national and global health policies and strategies. Since 1947, article 256 of the Universal Declaration of Human Rights, is clear on the right to health and many of its determinants. Around the same time, the World Health Organization was founded with the objective, as mentioned above, to achieve the best possible for all peoples. Such declaration took almost 20 years to translate in an international binding framework with right holders (citizens) and duty bearers (the States) in article 12 of the International Covenant of Economic, Social and Cultural Rights7. While only six countries have failed to ratify the Covenant8 -the United States amongst9-, only 18 have signed the optional protocol10, whereby countries are held accountable to an international committee where their citizens may report to if governments fail to comply. Similarly, only a minority of countries have committed to accountability on the other four main international treaties related to the right to health for specific groups (children, women, migrants and disabled). As a consequence, the recognition of the right to health remained like a nice intention, but not binding declaration for most, and the majority of world’s citizens cannot report anywhere at national or international level, when their basic health needs or access to adequate health services are not met. At the same time, the global agreements or initiatives that targeted only some health problems or some populations, had more visibility and political and social attention. At the turn of the Century, they influenced the setting of the non-right based health MDGs, while the general comment on the right to the Highest Attainable Standard of Health of the ICESCRs11 went largely un-noticed. Certainly, the shorter term of results of targeted initiatives (yet partial, discriminative and often short lived) provides the political credits during politician’s mandates and is more amenable to specific results and front-page news, than the longer term approach of establishing health right frameworks and universal and comprehensive health services which leave no people nor health conditions out.

6 http://www.un.org/en/documents/udhr/index.shtml#a25 7 https://treaties.un.org 8 https://treaties.un.org/pages/ViewDetails.aspx?src=TREATY&mtdsg_no=IV-3&chapter=4&lang=en 9 Economic, social and cultural rights: Questions and answers" (PDF). Amnesty international. p. 6. Retrieved 2 June 2008: "The United States signed the Covenant in 1979, under the Carter administration, but is not fully bound by it until it is ratified. For political reasons, the Carter administration did not push for the necessary review of the Covenant by the Senate, which must give its 'advice and consent' before the US can ratify a treaty. The Reagan and George H.W. Bush administrations took the view that economic, social, and cultural rights were not really rights but merely desirable social goals and therefore should not be the object of binding treaties. The Clinton Administration did not deny the nature of these rights but did not find it politically expedient to engage in a battle with Congress over the Covenant. The George W. Bush administration followed in line with the view of the previous Bush administration. The Obama Administration stated it does not seek action at this time on the Covenant". 10 https://treaties.un.org/pages/ViewDetails.aspx?src=TREATY&mtdsg_no=IV-3-a&chapter=4&lang=en 11 http://www.nesri.org/sites/default/files/Right_to_health_Comment_14.pdf


There is a deep contradiction of some high-income countries in championing the cause of health at global level yet not recognizing the universal right to health at home. Before more and more declarations of commitment in improving the health of all, many of them repeated in cycles during the last 65 years, and expensive international conferences and grand declarations, all member states in the world should recognize the universal right to health and accept international means of reporting and verification.

This challenge of the universal right to health involves the responsibilities of all, not just States and their institutions (beyond the shifts of governments). It involves citizens through the above mentioned balance of the individual and collective dimensions of health. It involves health professionals. Their shared code of ethics, the Hippocratic oath12, 2500 years old, or various adaptations, is sworn by most medical students upon graduation13. It was adapted in 1948 by the World Medical Association as the Declaration of Geneva (Physician's Oath) and amended in 1968, 1983, 1994, with editorial revisions in 2005 and 2006. The text14 still concentrates on the health professional´s duty for his or her individual patient. However, it has one sentence that related to equity15 (how many doctors engaged in private practice -over half of the world´s health professionals- commit to their oath of "preventing social standing to intervene between their duty and their patients"?), but it does not relate explicitly to the right to health. The health professionals role in the universal right to health, demands an additional sentence in the physicians oath when taking the responsibility of the health profession in the XXIst Century:

SETTING PRINCIPLES OF GLOBAL HEALTH In 1978 the International Conference of Alma-Ata16 agreed on principles to advance on the health for all peoples, with an emphasis on the democratization of health, recognizing that health was to be achieved for all peoples and by all people. Some years after, the Ottawa charter17 acknowledged that health was deeply related to all others policies and that it should be addressed in all policies. From the nineties, the reference to the world’s shared health challenges was progressively referred as global health, gradually replacing the term of international health and recognizing the growing diversity of factors and actors influencing the health of all nations and peoples.

12 Edelstein L. The Hippocratic oath: text, translation and interpretation. 1943. 13 Kaji S, Russell G, Fritz Z, Wong D, Rollin M, Dunning J, et al. Medical oaths and declarations. BMJ 2001; 323(7327): 1440-1441. 14 http://www.wma.net/en/30publications/10policies/g1/ 15 "I WILL NOT PERMIT considerations of age, disease or disability, creed, ethnic origin, gender, nationality, political affiliation, race, sexual orientation, social standing or any other factor to intervene between my duty and my patient" 16 http://www.who.int/publications/almaata_declaration_en.pdf

The first commitment in the international debates on health should be the recognition

of the right to health in a binding and accountable way.

"In my personal and professional capacities, I will preserve and promote the universal right to

health through health equity within and between generations".

17 http://www.who.int/healthpromotion/conferences/previous/ottawa/en/


p p p

FIGURE 1: USE OF GLOBAL HEALTH VS INTERNATIONAL HEALTH IN A SAMPLE OF PUBLISHED BOOKS.

The main principles of health agreed through the major international conferences in the last thirty years, may be summarized as health for all people (WHO constitution 1945), by all people (Alma Ata 1978) and in all policies (Ottawa). These principles18, however, are not reflected in many of the health initiatives claiming to be «global», while often restricted to some population groups or diseases -as even the Health MDGs- (not for all), by some central decisions taken vertically far from the targeted communities, in capitals or even in Geneva or New York (not by all) or focused on medical interventions and detached from structural changes in socioeconomic policies (not in all policies). Many of these not-really «global» initiatives concentrate a large share of the international resources19 for health and influence the international health agreements and commitments.

FIGURE 2: PRINCIPLES AND VECTORS OF GLOBAL HEALTH.

18 Garay J, Harris L, Walsh J. «Global health: evolution of the definition, use and misuse of the term», FACE À FACE. (Internet). 2013. Cited: April 23, 2014. Available from: http://faceaface.revues.org/745 19 McCoy D, Chand S, Shidar D. Global health funding: how much, where it comes from and where it goes. Health Policy Plan (Internet) 2009; 24(6): 407-417. Available from: http://heapol.oxfordjournals.org/content/24/6/407


The complexity of factors and actors influencing the world’s health, with the biased interests for some issues or groups, often prioritized in arbitrary ways and influenced more by lobbies than by evidence, are also a reflection of a weak democratic governance of international health today. If we’d wish nothing less for the democratic governance of health than what we demand for a democratic government in all countries, we’d wish a democratic forum of health representatives (ministers of health from democratic countries) which would gather in a parliament (World Health Assembly) and influence (according to their population weight and respecting minorities) international decisions. At present, Nauru’s vote at the World Health Assembly counts as much as China’s (with over hundred thousand times population). While minorities and small countries interests cannot be overshadowed by majorities or major countries, there needs to be some balance to respect the principle of equal weight of all persons in global agreements. The resolutions proposed for this biased vote, are linked to targeted funding coming from those with greater financial capacity (hence influencing the international health agenda) or some private groups or foundations (one20 being at present the largest founder of this meant-to-be international government of health). This is a common feature in our Washington consensus era, above mentioned, where the power and freedom of movement of capital -hence called “liberal policies”-, larger than those of persons, enables the influence of a few -dominating the capital- in the lives of most. What is most striking is that the funding to work on WHO governance reform comes from the main private donor21. A democratic WHO requires a budget through binding and regular contributions according to financial capacities (as in an equitable fiscal scheme).

A truly democratic World Health Assembly should aim at commitments and

resolutions into programs based on the health for all, by all and in all policies.

20 http://www.who.int/about/resources_planning/AnnexA67_43-en.pdf?ua=1 21 See chapter 4 of resolution 64.2, in annex 6: Financial and administrative implications for the Secretariat of resolutions adopted by the Health Assembly. Available from: http://apps.who.int/gb/ebwha/pdf_files/WHA64-REC1/A64_REC1-en.pdf#page=85


IV. EQUITY VS INEQUALITY

The Commission on Social Determinants of health calls on national governments to establish a national health equity surveillance system22, with routine collection of data on social determinants of health and health inequity; and calls WHO lead the creation of a global health equity surveillance system as part of a wider global governance structure. THE NEUTRAL STATISTICAL APPROACH Inequalities are a mathematic feature measured, in a large enough sample where quantitative variables tend to have a “normal” distribution, by the standard deviation (average differences with the mean value) and the dispersion index (the relation of the standard deviation, to the mean). In health we may see at international level the differences in life expectancy (LE). The figure below shows from the World Health Statistics data23 the evolution of the world average of national averages of life expectancies. It shows a steady increase of such average, from 53.8 in 1960 to 70.3 in 2012, at an average rate of some 115 days of annual increase in life expectancy at birth, that is, almost an increase of one day of higher LE every three days. This is surely the highest rate of increase in life expectancy through humankind time. Such privilege needs to be seen with responsibilities.

FIGURE 3: EVOLUTION OF WORLD AVERAGE LIFE EXPECTANCY.

Let us look at the inequality hidden behind the global average figures. If we look at the difference between the extremes, we see that the best performers (Nordic countries until the 90s and thereafter Japan) have steadily increase their LE levels while the worst performers have fallen abruptly in cases of genocides (Cambodia in the 70s, Rwanda in the 90s) or epidemics (AIDS in Zimbabwe from the late 90s).

22 http://www.who.int/social_determinants/thecommission/finalreport/en/ 23 http://www.who.int/gho/publications/world_health_statistics/2013/en/


FIGURE 4: DIFFERENCE IN NATIONAL LIFE EXPECTANCY BETWEEN EXTREMES.

But the difference between extremes may not be representative of the dispersion of data. Below we show the graphs representing the world´s population distribution according to their national average life expectancy by 5 and 1 year LE intervals in 2012.

FIGURE 5: DISTRIBUTION OF NATIONAL AVERAGE LIFE EXPECTANCY LEVELS, 5 YEARS INTERVALS, 2012.


FIGURE 6: DISTRIBUTION OF NATIONAL AVERAGE LIFE EXPECTANCY LEVELS, ONE-YEAR INTERVALS, 2012.

The distribution of the world´s population by their national average LE shows three main peaks at 67 years (India), 71 years (Russia, Bangladesh and Indonesia) and at 75 (China). Even of smoothed, the curve shows a skewed distribution towards lower values.

FIGURE 7: COEFFICIENT OF VARIATION AND RATIOS OF QUARTILES OF LE, OVER TIME.

FIGURE 8: MEDIAN-MEAN GAP OF LE OVER TIME.


The analysis of dispersion amongst national life expectancies has the limitation of the validity, the internal dispersion and the different weight of populations we should consider in each national average. Notwithstanding these limitations, the analysis seems to indicate that the dispersion (differences) of national life expectancies have diminished over time although the rate of decrease has slowed down since the mid-80s. In the second half of the last decade, the dispersion of national life expectancies seems to shrink again, mainly due to the decrease of the ratios of extremes rather than the dispersion of the mid quarters. This may be due to the increase of life expectancy of China and the narrowing of the differences of best and worst levels of under-5 mortality (with a strong influence on life expectancy), like the trends of decrease of such mortality rates in the best performing countries that are reaching a plateau at very low rates.

FIGURE 9: DIFFERENCE OF LOWEST AND HIGHEST UNDER-5 MORTALITY RATES OVER TIME.

The percentage gap between median and mean shows a reduction from the 50s till the 70s, followed by a slightly growing rate since the mid-70s and stable since the 90s in favor of a median higher than the mean reflecting concentration of greater number of countries under the lower half of the world average life expectancy. Another way to represent the dispersion of values (inequalities) and which takes into account the population of each national average is the Gini curve, normally used in income distributions24. It shows the divergence between the egalitarian distribution (all people with identical life expectancy values) and the cumulative values of countries population by their life expectancies. The graphs below show that comparison and the surface under the egalitarian line, known as Gini coefficient.

FIGURE 10: GINI CURVE DISTRIBUTION OF NATIONAL AVERAGE LIFE EXPECTANCIES, 2012. 24 Gini index measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution (http://data.worldbank.org/indicator/SI.POV.GINI).


FIGURE 11: GINI COEFFICIENT OF THE DISTRIBUTION OF NATIONAL AVERAGE LIFE EXPECTANCIES, OVER TIME.

The evolution of the Gini index of life expectancy shows a decline until the mid-80s and a stable rate around 4% since then, with a mild decrease in the last decade.

FIGURE 12: INEQUALITY OF GINI COEFFICIENT AND ASYMMETRY RATE, OVER TIME.

As with the normal distribution´s dispersion and asymmetry figures, the inequality of Gini coefficients (estimated by the standard deviation of the distribution of differences of each cumulative population x life expectancy with the egalitarian line) and its asymmetry (median- mean gap of the mentioned distribution) shows a stable inequality, but shifting from a higher median-than-mean scenario before the 80s to slightly lower thereafter. This means an evolution to a skewed distribution of the inequality towards lower values. In summary, the statistical analysis of the international distribution of national average LE in the last 50 years reflects the highest increase in human history, lowering dispersion of LEs until the 80s and stable thereafter with a stable skewed distribution towards lower values. In a few words, us, the most privileged -concerning quantity of life- of the 60,000 human generations, has a high degree of inequality skewed to lower values (concentration of higher LE in fewer countries and population) since the 80s.

Our generation enjoys the highest ever life expectancy in humankind history, but with a

stable degree of inequality since the 80s, with a concentration of higher LE in fewer countries and population.


MAIN HEALTH INFLUENCING VARIABLES By the turn of the century, WHO´s controversial world health report 2000 estimated health inequalities through complicated mathematical formulas and disconnected from any variable which would enable analysis of its root causes. Moreover, such estimates were used to rate health systems performances and did not incorporate the critical dimension of their approach to ensure health equity. In order to identify the main variables influencing health, we reviewed the international reports on health risks. In 2002 the World Health Report reviewed the main risks associated to ill health25. Twenty six major risks were selected and their attribution (attributable-risk) to the diseases and the burden of ill health (measured in Disability Adjusted Life Year -DALYs-) was estimated. The list did not identify a hierarchical structure of causes and included in the same level, for instance, zinc deficiency and climate change. Ten years later, the report of the Global Burden of Disease 2010, selected 67 major health risks and their attributable burden of disease by years, regions, age groups and sexes26. Again such long list lacked a hierarchical structure of causes and some major causes of the previous report (2002), such as unsafe sex, were dropped from the list. The report on the Commission of Social Determinants for Health (CSDH) identified root causes of inequalities and focused on life conditions such as preschool care, healthy cities, decent jobs, universal and equitable health care, as well as equity in the economic, gender, governance, participation and trade27. These choices were also arbitrarily chosen and did not clearly include two main policy areas: legal frameworks of universal rights and the ecological determinants of health. Here we attempted to simplify and identify the main policy areas influencing health and aim at estimating the associated burden of health inequity. In order to structure the analysis of the main policy areas influencing health, we selected the main four domains which may include all policy areas at local, national and global levels. They can be summarized in policies aimed at ecological sustainability, economic equity, knowledge towards public goods and legal frameworks based on universal human rights.

25 http://www.who.int/whr/2002/en/ 26 http://www.healthdata.org/gbd 27 http://www.who.int/social_determinants/thecommission/finalreport/en/


Then, we selected sensitive indicators in each of these main policy domains and estimated the correlation power of those variables and life expectancy by time and across all countries average values28. The figure below shows the correlation between life expectancy and two indicators, the UN democratic index and the years of education as proxy indicators of the legal frameworks guaranteeing universal rights and the access to knowledge and opportunities in society.

FIGURE 13: CORRELATION BETWEEN DEMOCRATIC INDEX AND YEARS OF EDUCATION, AND LIFE EXPECTANCY.

When considering the weight of populations, the analysis of the correlation between the democratic index and life expectancy resulted in a weak correlation coefficient. The two main countries which include one third of the world’s population, China and India, had in fact a negative correlation: higher life expectancy in China with a lower democratic index, and the opposite case for India. In the case of education, there is a strong correlation between the years of education throughout life and the life expectancy at birth for countries with life expectancy below 60 years, but not so clear for countries with higher life expectancy29. On the other side, the quality of education varies greatly between countries, regions and times, and the analysis lacks consistency in that respect30. The high degree of “externality” of ecological policies and dynamics results in the contradictory fact that countries and groups in society with higher degrees of exhaustion of natural resources (measured for instance in carbon emissions per capita) are often less exposed to their related ill effects for health.

28 Data analysis based on UN data and indexes, World Health statistics for health indicators and World Bank data for economic indicators. 29http://www.academia.edu/289307/Examining_the_Relationship_Between_Life_Expectancy_Reproduction_and_Educational_Attainment_A_Cross-Country_Analysis 30 http://www.oecd.org/edu/eag2013%20(eng)--FINAL%2020%20June%202013.pdf


FIGURE 14: CORRELATION BETWEEN CARBON EMISSIONS PER CAPITA AND LIFE EXPECTANCY.

When we selected GDP pc31 for measuring economic policies, this was the most influential variable of national average life expectancy. The figure below shows a logarithmic strong relation with a correlation rate above 0.70, suggesting that over two thirds of the differences found in national average life expectancy may be explained by the differences in national average income (GDP per capita).

FIGURE 15: CORRELATION BETWEEN LIFE EXPECTANCY AND GDP PC.

31 The use of GDP per capita is controversial as the wealth produced/consumed per capita includes foreign companies and citizens which may concentrate and even flow out of the country, especially in low-income countries, a large proportion of resources. While GINI (which deducts those external flows and inversely counts the inflows from national citizens and companies abroad) may better reflect the citizens average wealth (and through GINI its distribution), it would not allow to set the challenge of real national and global redistribution, including for foreign investments and benefits, generally in favor of high-income countries. (See http://www.diffen.com/difference/GDP_vs_GNP).


Such correlation remains with some degree of variation over time and with other basic health indicators, such as healthy life expectancy, among men and women and with adult and child (reduced correlation) mortality rates.

The graph below shows how that correlation (linear) has evolved over time. It reflects a stronger correlation before the 80s and a stabilization round 0.6 since then. The trend of logarithmic curves over time shows a growing number of countries entering the plateau along which higher GDP pc do not translate in higher LE.

FIGURE 16: EVOLUTION OF THE CORRELATION BETWEEN LIFE EXPECTANCY AND GDP PER CAPITA.

INEQUALITIES ACROSS DISAGGREGATING VARIABLES When we compare health indicators between population groups disaggregated by the health influencing variables, we remain measuring differences and ratios. This has been the case of the international database of health "inequities" (really reporting on inequalities): the health equity monitor. The graph below, taken from WHO health equity monitor32, shows the distribution around median values of under-five mortality rates disaggregated by economic status (income) of the household (lowest to upper quintiles), level of the mother's education (non, primary or secondary), place of residence (urban or rural) and sex. 32 http://www.who.int/gho/health_equity/en/


FIGURE 17: ANALYSIS OF HEALTH INEQUALITIES OF UNDER 5 MORTALITY RATE IN WHO HEALTH EQUITY MONITOR.

Data are gathered from US-financed Household Demographic Surveys33 and UNICEF MICS34 in low and middle income countries, since 1993. In total, some 70 countries analysis was included and only 6 for 2012. In a more elaborated way, the so called "Marmot indicators" look at social determinants but still measure differences across variable disaggregated groups35. Other have looked at key socioeconomic influences in health and specific groups of diseases in the EU36, in Australia37 and in low and middle income countries38.This kind of analysis describes inequalities among subgroups, but cannot inform a common objective, measure the burden of health inequity, set the minimum income threshold and redistribution required and other transformational conclusions of measuring health inequity, as will be seen hereafter.

33 http://www.dhsprogram.com/Data/ 34 http://mics.unicef.org/surveys 35 http://www.instituteofhealthequity.org/presentations/presentation-slides 36 Dalstra JA, Kunst AE, Borrell C, Breeze E, Cambois E, Costa G, et al. Socioeconomic differences in the prevalence of common chronic diseases: an overview of eight European countries. Int J Epidemiol 2005; 34(2): 316-326. 37 Glover JD, Hetzel D, Tennant S. The socioeconomic gradient and chronic illness and associated risk factors in Australia. Aust New Zealand Health Policy 2004; 1: 8. 38 Hosseinpoor AR, Bergen N, Mendis S, Harper S, Verdes E, Kunst A, et al. Socioeconomic inequality in the prevalence of noncommunicable diseases in low -and middle- income countries: results from the World Health Survey. BMC Public Health 2012; 12: 474.


INEQUALITY VS INEQUITY Most proposals pretending to measure health equity really define statistical analysis of dispersions39. The statistical approach does not allow correlation with the underlying causes of health inequalities. Identifying fair limits of inequality through the distribution of the most influencing variable of health distribution, adds an ethical component. In 2003 Whitehead and Gruskin proposed as operational definition of health equity “the absence of systematic disparities in health between groups with different levels of underlying social advantage/disadvantage; that is, wealth, power, or prestige. In 2006 Paula Braveman proposed an updated definition: “Systematic health differences between socially advantaged and disadvantaged groups”40. If we combine the features of Whitehead41 and Braveman’s definitions, we would point at a threshold of health equity based on social differences which are unfair and avoidable. The following table describes the key differences and consequences of measuring inequality versus inequity.

TABLE 1: DIFFERENCES BETWEEN MEASURING INEQUALITY AND INEQUITY

Inequality Inequity

Concept Differences. Unfair differences.

Measurement Differences or ratios between subpopulations.

Gap from best feasible and sustainable level: burden of inequity.

Conclusions Arbitrary conclusions. Measurable objective, inter and intra national, intra and inter generational.

Strategy Approach to disadvantaged groups: poverty alleviation.

Approach to minimum thresholds: social cohesion (address both extremes), levels of dignity and universal rights.

Effect Mitigation. Transformation.

39 González GJ, Vega MG, Cabrera CE. Desigualdad social y equidad en salud: perspectivas internacionales. México: Universidad de Guadalajara, Centro Universitario de Ciencias de la Salud; 2010. p. 25-45. Available from: http://www.cucs.udg.mx/revistas/libros/DESIGUALDAD_SOCIAL_Y_EQUIDAD_EN_SALUD.pdf 40 Braveman P. Health disparities and health equity: concepts and measurement. Annu Rev Public Health (Internet) 2006; 27: 167-194. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16533114 41 http://jech.bmj.com/content/57/4/254.short

Equity is the fair distribution of inequality.


THE RELEVANCE OF ESTIMATING THE BURDEN OF HEALTH INEQUITY The setting of minimum and progressive thresholds has been considered by others as critical to enable the recognition and exercise of economic, social and cultural rights42. However, this approach never resulted in standardized thresholds43. The burden of ill health has been assessed for all diseases when comparing the effects of one disease event due to disability (by much debated arbitrary grading of disability and even different values to age periods) and live years lost due to premature death, with best national average level of healthy live years expectancy: Japan44. That method defined the unit of Disability Adjusted Life Year -DALY-. The methodology of burden of ill health by specific diseases has meant the basis of health economics by estimating the cost-utility (cost related to its impact on burden of ill health prevented/recovered) and opportunity-cost (the difference between options according to their cost-utility) of health preventive or therapeutic interventions. Such methodology -led by the World Bank- guided the prioritizations of health interventions based on their efficiency. This health econometrics fed the strategy of structural adjustments, led by World Bank: reducing public spending to address the national budget deficits and reducing the tax revenues considered as barriers to global flow of capital. It followed faithfully the conceptual framework of the Washington consensus45. The power of the conditions of World Bank loans influenced health policies and strategies aimed at reducing public spending and increasing its impact through specific cost-effective interventions. In the early 90s the threshold of efficiency for low income countries’ interventions with public budget (or loans) was at 30 US $/DALY46. In parallel, the growing evidence of HIV/AIDS triple therapy/life-saving treatments diluted that threshold. But the emphasis on specific cost-effective interventions (including consideration of externalities), started the still ongoing fragmentation of health services, their financing, organization and even health politics, activists, academics and organizations, during the last two decades. The selected views of diseases have led to today´s fragmentation which rather than advancing on the goal of the universal right to health, introduces another dimension of inequity across diseases. In that context, it is essential to estimate, as committed by all countries in the World Health Assembly´s resolution on social determinants of Health, the overall burden of health inequity, across and within countries47. 1. Burden of health inequity by best standards of influencing variables We hereby present how we attempted to estimate the burden of global health inequity in relation to standards based on the most influencing variables on health. 42 CEPAL. La hora de la igualdad: brechas por cerrar, caminos por abrir. 2010. Chapter 4. Available from: http://www.cepal.org/publicaciones/xml/0/39710/100604_2010-114-SES.33- 3_La_hora_de_la_igualdad_doc_completo.pdf 43 Asada Y. A framework for measuring health inequity. Journal Epidemiol Community Health (Internet) 2005; 59: 700-705. Available from: http://jech.bmj.com/content/59/8/700.full.pdf+html 44 http://www.who.int/healthinfo/statistics/GlobalDALYmethods_2000_2011.pdf?ua=1 45 http://www.who.int/trade/glossary/story094/en/ 46 Investing in health. The World Bank report. 1993. 47 See WHO 62.14 in http://apps.who.int/gb/ebwha/pdf_files/WHA62-REC1/WHA62_REC1-en-P2.pdf


As income (GDP pc) was found to be the strongest associated factor influencing inequalities in national average LE, we took as the reference level of best health those of the countries with GDP pc in the upper quintile, almost identical to the World Bank classification of "high income countries". Once we concluded that the mean health indicators of high income countries are the health standards desirable globally (WHO’s original objective “best health for all”), we could estimate, as others have done at subnational level48, the health inequity burden: the difference between the present health situation and the desired one. Any information on the progress or failure on the objective of health equity at country or global levels, needs to be easy to understand and interpret by statisticians, health professionals, policy makers, politicians and civil society alike. This is why we aimed at measuring the burden of health inequity against the mentioned reference (high income countries) in excess deaths ("avoidable" if there were economic equity). We used under-five and adult mortality rates because there are national average data of both and over 90% of the premature deaths take place during the first five years of life and from 15 to 60 years of age. Hence we then applied -with data from the world health statistics annual reports- the desired high income adult and under-five mortality rates to the under-five and adult populations of the equivalent population groups of other than high income countries (adjusted mortality rates). When we compare that figure with the present levels of under-five and adult deaths in low and middle income countries, the estimates of excess mortality due to global inequity is close to 20 million deaths per year, over half of them in children under-five. This represents over one third of all deaths and the numbers and proportions have remained stagnant over the last two decades. The following graphs show those figures and proportions.

FIGURE 18: AVOIDABLE DEATHS DUE TO ECONOMIC GLOBAL HEALTH INEQUITY (REF. HICS).

48 Esnaola S, Aldasoro E, Ruiz R, Audicana C, Pérez Y, Calvo M. Desigualdades socioeconómicas en la mortalidad en la Comunidad Autónoma del País Vasco. Gac Sanit 2006; 20(1): 16-24.


FIGURE 19: NUMBER OF AVOIDABLE DEATHS (MILLIONS) BY GLOBAL ECONOMIC INEQUITY 1990-2012 (REF HICS).

FIGURE 20: PERCENTAGE OF ALL DEATHS DUE TO GLOBAL HEALTH INEQUITY DEATHS 1990-2012 (REF HICS).

When the data of the GBD 2010 were made public, with national average mortality rates since 1970 by five year age groups, the same estimates were analyzed, this time with a much finer age adjustment of mortality rates by 190 countries, during 40 years and across 16 age groups each country and year. The results are very similar to the more gross estimates based on under-five and adult mortality rates alone form the world health statistics, with a slightly higher share of all deaths due to global economic inequity (over 40%) and since 1970. They seem to confirm the hypothesis of a very high and stagnant level of health inequity49. 49 https://apha.confex.com/apha/141am/webprogram/Paper291133.html


FIGURE 21: NUMBER OF AVOIDABLE DEATHS DUE TO GLOBAL ECONOMIC INEQUITY 1970-2012 (SOURCE: GBD 2010).

FIGURE 22: PERCENTAGE OF DEATHS DUE TO GLOBAL ECONOMIC INEQUITY 1970- 2010.

While this method does indicate the effects of economic inequity on health and measures it in avoidable deaths, it has a number of weaknesses: It arbitrarily chooses a cut-off point (e.g. top quintile) as the standard of best health levels. While we checked on feasible models (countries below world average GDP pc with life expectancy

within the confidence interval of high income countries), the method has one major contradiction: the standard levels of health are determined by health models with a concentration of economic resources which is not only non/replicable at global level, but also partly the reason why many others remain under income levels incompatible with those best health levels.

It ignores the dimension of ecological sustainability, related to intergenerational equity, as will be seen in the following sections.


2. Burden of inequity by thresholds of feasibility and sustainability This third approach (which we hereby propose) is based on the definition of healthy, replicable and sustainable thresholds models across and within countries, avoiding arbitrary cut-off points and proposing levels of health truly replicable for all and sustainable for coming generations. Among the four major policy areas influencing health (ecologic sustainability, economic equity, participatory knowledge and legal frameworks on universal human rights) there are differences in the resources available. The very conceptual nature of legal frameworks relates to universality. The participatory knowledge should avoid limits to human creativity, innovation and participation. However, economic and ecologic resources are limited and their use and distribution meets feasibility and sustainability thresholds. In the case of economic resources, no country with average GDP pc above the world’s weighted average can serve as a globally replicable model, as there would not be -arithmetically- enough resources for all. Moreover, the growth thesis whereby "economically" successful countries guide global progress and development is in contradiction with equity, sustainability and even human wellbeing (see later chapter VI). The previous methodology based on standards of high income countries is therefore flawed by this contradiction. As for ecologic resources, no country with carbon footprint or use of hectare per capita and year above the planetary boundaries may serve as a standard to estimate health equity, as the ecologic -and all others derived from them, including economic- resources would be exhausted for coming generations and so would their health be compromised. We therefore selected countries with national average LE above the global weighted average and economically feasible and ecologically sustainable models. We used for this purpose the World Bank data on life expectancy, GDP pc and population from 1960 till 201250. The first screening was to find out the weighed (according to population) average of national life expectancies and select countries with life expectancy above the world average (“healthy countries”). The second screening aimed at calculating the weighed (according to population) average of GDP pc and select countries with life expectancy above world average and also GDP pc below the world average (“healthy feasible countries”). The third screening meant selecting from the above screened health feasible countries those with carbon emissions per capita below the planetary boundary, estimated for the decade 2000-2010 on average 2.5 metric Tons per capita and year51. The following maps52 show the countries (in dark green) that belonged in 2010 to each level of screening towards the healthy-feasible-sustainable (HFS) models:

50 http://data.worldbank.org/ 51 http://www.nature.com/nature/journal/v461/n7263/fig_tab/461472a_T1.html 52 These maps are interactive during period 1960-2012 in the Onix Institute webpage.


FIGURE 23: MAP OF COUNTRIES IN 2010 ABOVE THE WEIGHTED WORLD AVERAGE OF LIFE EXPECTANCY.

FIGURE 24: MAP OF COUNTRIES IN 2010 WEIGH GDP LOWER THAN WORLD WEIGHTED AVERAGE.

FIGURE 25: COUNTRIES WITH CONSTANT RATE OF CO2 EMISSIONS PER CAPITA BELOW PLANETARY BOUNDARY.


The number of countries belonging to each screened category and throughout 1960-2012 is represented in the following graph.

FIGURE 26: NUMBER OF COUNTRIES ACCORDING TO CATEGORIES OF HEALTHY, FEASIBLE AND SUSTAINABLE CRITERIA, 1960-2012.

The following graphs show the distribution of national average life expectancies and of carbon emissions per capita according to the national average GDP pc levels and the economic, healthy and sustainability thresholds.

FIGURE 27: LIFE EXPECTANCY ACCORDING TO GDP PC AND THRESHOLDS OF HEALTHY AND EFFICIENT (FEASIBLE AND EQUITABLE) STANDARDS, 2012.

FIGURE 28: CARBON EMISSIONS PER CAPITA ACCORDING TO GDP PC AND THRESHOLDS FOR FEASIBLE AND SUSTAINABLE STANDARDS, 2012.


The final selection of countries which have had between 1960-2012 constant features of life expectancy above world’s weighted average, GDP pc below world’s weighted average and carbon emissions per capita below planetary boundaries are represented in the following map, while listed with their threshold features in the following chapter.

FIGURE 29: CONSTANT (1960-2012) HEALTHY-FEASIBLE-SUSTAINABLE (HFS) MODELS.

Features of the healthy, feasible and sustainable model countries The 14 countries which represent average healthy, feasible and sustainable models add together close to 200 million people, some 3% of the world’s population; almost half living in Vietnam and over 80% in the three largest countries: Vietnam, Colombia and Sri Lanka.

FIGURE 30: POPULATION OF 14 HFS. Life expectancy The average life expectancy of the fourteen constant healthy-feasible-sustainable (HFS) (14 HFS) countries is 7-10% higher than the world weighted average (by countries’ population sizes), although it is 10% lower than the world’s best performing country: Japan. Among them, Cuba has had the highest life expectancy on average 1960-2012 and Costa Rica since 1980, reaching in 2012 the top 5% of national LE levels, in fact higher than high income countries.


TABLE 2: LIFE EXPECTANCY OF 14 HFS COUNTRIES

Country name Average 1960-2012 Average 1980-2012 2012

Cuba 71.17 73.52 77.35

Costa Rica 70.24 70.79 74.44

Albania 69.06 71.25 73.70

Armenia 66.64 69.92 73.78

Georgia 73.15 76.72 79.71

Belize 73.70 76.08 79.07

St. Lucia 69.66 71.19 73.94

Sri Lanka 67.34 69.58 72.61

Vietnam 67.92 69.36 72.19

Tonga 68.42 70.92 74.07

St. Vincent 68.53 71.67 74.67

Paraguay 68.06 70.34 72.40

Grenada 68.22 70.23 72.49

Colombia 68.24 72.08 75.61

Average 14 HFS 69.31 71.69 74.72

Weighted average 68.61 71.72 75.13

World average 63.28 66.30 70.62

Best country 78.18 80.13 83.48

The overall evolution of the 14 HFS countries shows how after Cuba Rica and Armenia topped LE up to the sixties, Costa Rica joined Cuba in the 70s and them both remained with the highest values, with Armenia dropping its life expectancy in the 80s, as did Vietnam in the 70s, due to the effects of dramatic wars.

FIGURE 31: EVOLUTION OF 14 HFS 1960-2012.


The breakdown by sex shows that while Costa Rica and Cuba have the highest levels throughout time, Vietnam and Albania also surpass the 80 years of life expectancy for women, among the highest in the world. As for men, Costa Rica and Cuba also have the best values, followed by Albania and the rest.

Gross Domestic Product per capita GDP per capita varies widely (by a ratio of 5-6) in the 14 HFS countries. It is higher (“higher cost of life”) in Central America and the Caribbean, followed by South America (yet much higher in Colombia than Paraguay), the Balkans and the Pacific, and lowest in Asia, with the most efficient model, by far, in Vietnam. On average, the 14 HFS countries have a GDP pc half of the world’s weighted average.

TABLE 3: GDP PC OF 14 HFS

Country 1960-2013 2000-2013 2013

Vietnam 626 989 1911

Sri Lanka 725 1745 3280

Paraguay 1374 2328 4403

Armenia 1513 2233 3505

Georgia 1516 2014 3602

Tonga 1773 2956 4427

Albania 1832 3159 4652

Colombia 1981 4535 7826

Belize 2126 4256 4834

St. Vincent 2195 5438 6634

Costa Rica 2800 6111 10185

Cuba 2832 4624 6825

Grenada 3730 6703 7876

St. Lucia 3947 6050 7309

14 HFS average 1747 3796 5519

World average 3656 7461 10256

World max 66244 127809 111162


The evolution of the GDP pc shows that Cuba had the highest GDP pc of the 14 HFS countries until the early 80s, when Costa Rica gradually took the lead and surpassed the Caribbean islands. Costa Rica and Colombia have increased their GDP pc very steeply in the last five years and the former is reaching the world’s average, hence could soon be leaving the HFS group. In the lower -most efficient- end, Georgia, Tonga, Colombia and Paraguay had decreasing levels in the 80s and 90s and all have picked up in the last decade. Vietnam has consistently been the most health-efficient country among the 14 HFS, with GDP pc 5 times lower than the world’s average -as reflected in the previous section- and a 10% higher life expectancy during the last 20 years.

FIGURE 34: EVOLUTION OF GDP PC OF 14 HFS, 1960-2013.

CO2 emissions per capita The map below shows that the 14 HFS countries are in the lower half of polluting countries and far lower than the top 20 countries with highest CO2 emissions (above 7 metric tons per capita -see map-), responsible for most of the global warming.

FIGURE 35: WORLD DISTRIBUTION OF CO2 EMISSIONS PER CAPITA, 2000-2010.


As the graph below shows, the available data on CO2 emissions per capita (only since 2000) show that the world’s average is reaching twice the level of the planetary boundary. Among the 14 HFS countries, Cuba is close to the planetary boundary in the last years and could soon be leaving that group of countries if the trend of growth on carbon emissions continues. The most sustainable countries among the 14 HFS are Paraguay and Sri Lanka with Belize joining in the last years.

FIGURE 36: EVOLUTION OF CO2 EMISSIONS IN 14 HFS, 2000-2012.

In summary, the 14 HFS countries have an average 7% higher life expectancy, 50% lower GDP pc and CO2 emissions than the world’s weighed per capita average. The most effective -highest LE- models in the last 60 years are Costa Rica and Cuba (with average 10 years longer life expectancy than the world average), the most efficient -lowest GDP pc- is Vietnam (5 times lower than world’s average), while the most sustainable -lowest CO2 emissions- are Sri Lanka and Paraguay (lower than half the planetary boundary). There is a risk that the two most effective countries trespass the feasibility (Costa Rica reaching the world’s GDP average) and sustainability (Cuba surpassing the planetary boundary on CO2 emissions per capita) limits and leave the HFS group.


V. THE BURDEN OF GLOBAL HEALTH INEQUITY

Following the methodology described in the previous chapter, we compared the health levels of the reference healthy-feasible-sustainable (HFS) countries, taken as the objective of best feasible (and sustainable) state of health, applied to all countries, with the countries’ real values. BURDEN OF INEQUITY IN LOSS OF LIFE EXPECTANCY The following maps show the evolution of the difference of average national life expectancy with the standard HFS models.

FIGURE 37: MAP OF LE GAP FROM HFS STANDARDS IN 1960.




As the maps show, the countries with more than 5 years of life expectancy above the healthy-feasible-sustainable (HFS) models have shrunk from 1960 to concentrate in the EU15 + Norway, Switzerland and Iceland, Australia and Nez Zealand, Japan and South Korea, and Canada, all of them countries with accumulation of economic resources far above the average (and in the hoarding area, as will be shown later) and exhausting natural resources from coming generations. On the other extreme, countries with more than 10 years less than the life expectancy of HFS models were initially spread from the Andean region and Central America, the whole of Africa, the Middle East and almost all Asia, south to the Soviet Union, and are now concentrated in sub-Saharan Africa, Yemen, Afghanistan and Papua New Guinea, yet with still many countries in Central and South Asia, and Bolivia, 5 to 10 years below the standards. Annex 1 shows the table of countries by decades 1960-2012 with the gap of life expectancy with the HFS models as a measure of burden of global health inequity in LE loss. BURDEN OF GLOBAL HEALTH INEQUITY IN AVOIDABLE DEATHS While the loss of life expectancy reflects the effects of global inequity from feasible and sustainable models accumulated through life time with an effect on life years, as mentioned in the previous chapter, the notion of avoidable deaths due to global health inequity is easier to understand across countries and groups in society. It also allows disaggregating in age groups and provides a finer analysis of the evolution of such gap across society in time. We used the following methodology to estimate the number of avoidable (and unfair) deaths due to global health inequity:

1. We took the UN statistics database on demography53 which estimates annual average population

and number of deaths by 5 year period from 1950 till 2010, by 5 year age group (from 0 to 80)54 and by sex.

2. We estimated the annual average mortality rates by five-year period (1950-2010), age group and sex, of the 14 healthy-feasible-sustainable (HFS) models described in the previous section (from now on called 14 HFS rates).

3. We then equally estimated the annual average mortality rates by five-year period (1950-2010), age

group and sex, for each of the countries in the world. 4. The previous analysis allowed the calculation of the aim (WHO 1945 objective) of best feasible

health for all measured in number of deaths, by applying the 14 HFS rates to the disaggregated (period, age and sex) populations of each country.

5. We could then calculate the difference between the aimed best feasible/sustainable number of

deaths and the actual numbers disaggregated by country, period, age and sex. 6. By adding all excess deaths due to global health inequity, we calculated the global burden of health

inequity.

7. The relative burden of global health inequity was defined by calculating the proportion of all deaths (in each country, period, age group and sex) which were due to global health inequity.

53 http://unstats.un.org/unsd/snaama/dnllist.asp 54 Above 80 years of age data are not available for all countries and periods of time.


8. By giving a value of life years lost to each avoidable death when comparing their age of death with

the average 14 HFS life expectancy in each year and sex, we could also estimate the burden of global health inequity measured in life years lost/year.

9. The above data were represented in interactive bar charts, survival pyramids and world maps. Number of avoidable deaths due to global health inequity

Following the above methodology, the total number of avoidable deaths in the world (adding all avoidable -excess- deaths) by annual average of five-year periods 1950-2010 is as the following table, graph and maps show.

TABLE 4: TOTAL NUMBER OF ANNUAL AVERAGE AVOIDABLE DEATHS BY 5 YEAR PERIODS, 1950-2010

Period Global avoid deaths

1950-1955 22.378.571

1955-1960 22.359.125

1960-1965 23.681.287

1965-1970 17.540.402

1970-1975 15.953.937

1975-1980 16.333.040

1980-1985 16.360.286

1985-1990 16.561.451

1990-1995 17.603.116

1995-2000 18.591.914

2000-2005 17.428.328

2005-2010 16.821.851

Geographical distribution Annex 2 lists all countries and their estimates of avoidable deaths each tenth year from 1950 till 2010. The following graphs show the trend of the main countries hosting avoidable deaths and their share in 2005-2010.


FIGURE 40: EVOLUTION OF AVOIDABLE DEATHS IN THE COUNTRIES WITH HIGHER NUMBERS, 1950-2010.

As the table above shows, the annual number of avoidable deaths decreased from 22.3 million in the 50s and mid-60s, to some 16 million thereafter, with an increase by the turn of the century to 18.5 million and further drop thereafter to return to the level of around 17 million. As the graph shows, China had an abrupt decrease from the 60s till the mid-70s and another drop after 1995, which meant that by the turn of the century China had no more avoidable deaths due to GHiE, even before China surpassed the GDP pc of the 14c HFS models. The drops in the global number in the 60 and after the turn of the century can be largely attributed to China´s decrease, while the increase in the 90s and around the turn of the century may be attributed mainly to the increase of Nigeria and South Africa (largely due to the AIDS pandemic) and of Russia (especially middle aged men´s mortality after the collapse of the Soviet Union). The pie chart below shows the present distribution of avoidable deaths in the countries with higher number, showing one quarter of them in India, and another quarter in Nigeria, DRC, Russia and South Africa together.

FIGURE 41: DISTRIBUTION OF AVOIDABLE DEATHS BY GHIE BY THE COUNTRIES WITH HIGHER NUMBERS, 2005-2010.


Distribution of avoidable deaths by age groups, sex and time The distribution and evolution by age groups shows how the number of avoidable deaths in under-5s increased in the 50s and mid 60s and since then has steadily decreased, so the number in 2005-2010 was half the level of 1950-1955. That decrease of some 5 million avoidable under-5 deaths is the main difference in the overall number of avoidable deaths above mentioned. Regarding the other age groups, after a slight decrease in the number of avoidable deaths in middle and third age groups from 1950 to 1970, those figures kept rising since 1980, particularly in the age groups over 50.

FIGURE 42: DISTRIBUTION BY AGE GROUP AND EVOLUTION IN TIME OF TOTAL NUMBER OF AVOIDABLE DEATHS BY TOTAL HEALTH INEQUITY.

FIGURE 43: DISTRIBUTION BY AGE GROUP AND SEX AND EVOLUTION IN TIME OF TOTAL NUMBER OF AVOIDABLE DEATHS DUE TO GLOBAL HEALTH INEQUITY.


The difference of the distribution and trend of avoidable deaths amongst age groups, between sexes, reflects a higher number overall in men until the turn of the century, when women’s avoidable deaths overcame those of men, which decreased in the last decade. Such difference is mainly due to the increase of avoidable deaths in middle aged and elderly women, which compensates for the decrease of avoidable deaths in under-5 girls. PROPORTION OF DEATHS WHICH ARE AVOIDABLE The number of avoidable deaths is misleading as it does not relate to the overall number of deaths in each sex, age group and year. The following table, maps and graphs shows the distribution of those proportions.

TABLE 5: PROPORTION OF DEATHS WORLD WIDE WHICH ARE AVOIDABLE BY GHE

Period Proportion of deaths which are avoidable by GHE

1950-1955 46.88%

1955-1960 47.58%

1960-1965 49.27%

1965-1970 42.37%

1970-1975 39.32%

1975-1980 40.87%

1980-1985 40.61%

1985-1990 40.67%

1990-1995 42.09%

1995-2000 43.49%

2000-2005 40.64%

2005-2010 39.38%

As the table shows, the proportion of deaths worldwide which are avoidable reduced from 1950 till 1970 and thereafter stabilized around 40%. Geographical distribution of the proportion of deaths which are avoidable by GHE Annex 3 shows a table of all countries with the proportion of deaths which are avoidable by 5 year periods from 1950 to 2010. The following maps also show the geographical distribution of the proportion of deaths which are avoidable by global health equity and disaggregated in 0-15, 15-60 and > 60 year old age groups. They show how such proportion in the under-15 year age group has evolved from 1950 with a high proportion (over 30%) in Mesoamerica and the Andean Region, the whole of Africa, Middle East and almost all of Asia, except Russia and Japan, to 2010, where those high proportions are limited to parts of Central America and the Andean Region, Africa, Middle-East, India, South Asia, with a lower proportion in many Arab countries, and in China and Mongolia.


FIGURE 44: PROPORTION OF DEATHS WHICH ARE AVOIDABLE IN <15 YEAR OLD, 1950-1955.

FIGURE 45: PROPORTION OF DEATHS WHICH ARE AVOIDABLE IN <15 YEAR OLDS, 2005-2010 As the following maps show, the proportion of deaths which could be avoidable by global health equity in the 15-65 year old group has evolved from 1950-1955, where parts of Central America and the Andean Region, most of Africa (exceptions of Zimbabwe and Egypt) and Middle East and Asia had proportions >40%, to 2005-2010 where such proportion shrunk in Latin America to Bolivia and Guyana, to sub-Saharan Africa and India and South Asia (except Indonesia) and Russia (mainly due to men´s high avoidable mortality).

FIGURE 46: PROPORTION OF DEATHS WHICH ARE AVOIDABLE IN 15-65 YEAR OLDS, 1950-1955.


FIGURE 47: PROPORTION OF DEATHS WHICH ARE AVOIDABLE IN 15-65 YEAR OLDS, 2005-2010.

In the over-65 year old group the proportion of avoidable deaths due to global health equity was over 30% in less countries than in younger age groups: Bolivia, parts of sub-Saharan Africa, Middle East and India, China and Mongolia; whereas in 2005-2010 such proportion affected only to Guyana in Latin America, but expanded to most of Africa, Russia, the "Stans" and Indonesia. China was lowering such proportion.

FIGURE 48: PROPORTION OF DEATHS WHICH ARE AVOIDABLE IN <65 YEAR OLDS, 1950-1955.

FIGURE 49: PROPORTION OF DEATHS WHICH ARE AVOIDABLE IN >65 YEAR OLDS, 2005-2010.


In summary, and as the maps below show for all age groups, the proportion of all deaths which would be avoidable by global health equity reflects that in 1950-1955 more than one third of all deaths would have been avoidable in Central America and South America, except their southern countries in both cases, all of Africa, Middle East and Asia except Russia and Japan. Such distribution evolved progressively till 2005-2010, when only Bolivia, Guatemala and Guyana in Latin America had that proportion, as did sub-Saharan Africa while North Africa, most of the Middle East and China left that situation, while Russia and other ex-USSR countries increased their burden of inequity to that level.

FIGURE 50: PROPORTION OF ALL DEATHS WHICH ARE AVOIDABLE BY GHE, 1950-1955.

FIGURE 51: PROPORTION OF ALL DEATHS WHICH ARE AVOIDABLE BY GHE, 2005-2010. Distribution of the proportion of deaths which are avoidable, by age groups, sex and time The evolution and distribution of the proportion of avoidable deaths by age groups shows an increasing proportion in under-15s except in the 60s-70s, decreasing in older than 15 years until the mid-80s, when it starts increasing again. In fact, the overall pattern is an increase since the 50s, with a turning point to an increasing trend which starts earliest, according to age groups (<15 in the 60s, 15-50 in the 70s and > 50 in the late 80s).


FIGURE 52: EVOLUTION AND AGE DISTRIBUTION OF THE PROPORTION OF AVOIDABLE DEATHS.

The disaggregation of the proportion of avoidable deaths by sex and age group shows, in the first and the last recorded period, a trend from a higher proportion of avoidable deaths in men in 1950-1955 to a higher in women in 2005-2010 (over 50% versus 30% in men) and for all age groups, with especially high difference in the reproductive age groups (15-40 years) and also in ages above 50 years, where men’s proportion of avoidable deaths gradually decreases while it remains high in women. The evolution of age distribution has shifted from a more even distribution, but with higher proportions in the 50s, to a greater concentration of the proportion of avoidable deaths in younger groups in 2005-2010, principally in males.

FIGURE 53: SEX AND AGE DISTRIBUTION OF THE PROPORTION OF AVOIDABLE DEATHS, 1950-1955.


FIGURE 54: SEX AND AGE DISTRIBUTION OF THE PROPORTION OF AVOIDABLE DEATHS, 2005-2010.

BURDEN OF GLOBAL HEALTH INEQUITY BY LIFE YEARS LOST As the proportion of deaths which are avoidable by GHE qualifies the net number of avoidable deaths in each age, sex and country situation and gives a better estimate of the burden of health inequity, the estimate of life years lost due to premature death in turn due to global health inequity, gives an even more accurate measure of the burden of health inequity.

TABLE 6: BURDEN OF GLOBAL HEALTH INEQUITY IN LIFE YEARS LOST DUE TO

PREMATURE DEATH, 1950-2010

Period Est total LYL

(GBD 2000-2012)

LYLs x ineq GI % of LYL due to GHiE Population

LYL pc-y due to GHiE (in days)

1950-1955 523.061.410 2.812.128.132 67.89

1955-1960 634.511.137 3.199.903.317 72.38

1960-1965 771.676.573 3.542.162.207 79.52

1965-1970 678.823.240 3.921.030.792 63.19

1970-1975 670.783.737 4.292.543.844 57.04

1975-1980 677.787.215 4.684.568.499 52.81

1980-1985 677.720.360 5.111.098.313 48.40

1985-1990 688.617.471 5.551.862.639 45.27

1990-1995 690.111.246 5.965.482.560 42.22

1995-2000 690.555.496 6.360.035.459 39.63

2000-2005 2.185.575.930 637.973.314 29.19% 6.752.392.208 34.49

2005-2010 2.064.067.256 606.818.689 29.40% 7.155.578.115 30.95


As the table above shows, the overall burden of life years lost due to global health inequity has increased from over 500 million in 1950-1955 to over 600 million in 2005-2010. The proportion of those life years lost in relation to the life years lost estimated in the Global Burden of Disease (calculated against best life expectancy and also lowest share of disability -Japan for 1990- and a combination of lowest death rates for the GDB 201055) has been around 30% in the last decade. Such loss of life must be related to the overall population: while in 1950-1955 the loss was in the range of 70 days loss of life per person and year (some 15% of all the life potential), that loss of life per capita and year was gradually reduced to some 30 days in the period 2005-2010. DISTRIBUTION OF AVOIDABLE LIFE YEARS LOST BY AGE GROUPS, SEX AND TIME

As seen in the graph below, the age distribution of LYLs due to GHiE has evolved between 1950 and 2010, so that while the LYLs in under-5s still hold the greater proportion, this share has been decreasing since the 90s and increasing in older age groups, however the net effect is a 15% decrease in life years lost since the 90s.

FIGURE 55: EVOLUTION 1950-2010 AND AGE DISTRIBUTION OF THE LIFE YEARS LOST DUE TO GLOBAL HEALTH INEQUITY.

The burden of LYL due to GHiE and its age distribution when disaggregated by sex shows that the overall burden is much higher in women (some 40% higher) and such burden difference increases with age. 55 http://www.who.int/healthinfo/statistics/GlobalDALYmethods_2000_2011.pdf


FIGURE 56: DISTRIBUTION OF LYL DUE TO GLOBAL HEALTH INEQUITY, BY SEX, IN 2005-2010. DEMOGRAPHIC PYRAMID DEVIATIONS DUE TO GLOBAL HEALTH INEQUITY The estimates above may also be represented in the demographic pyramid. When we compare the structure of the demographic pyramids of the GHE 14 HFS models with the world´s demographic pyramid for the period 2005-2010, we see that in women almost 10% less reach their 80th birthday due to GHiE. Such loss along the way is gradually spread through each age period with half of the loss, in fact after the 60th birthday. The same comparison in men shows that the difference between the 14 HFS models and the world´s demographic pyramid is more subtle, with only 3% more of men surviving the 80th birthday in the standards than the world´s average and most of that loss taking place before the 50th birthday. In analyzing these differential demographic pyramids we need to take into account that the comparison between the HFS models and the world´s overall population is misleading ads the latter contains both, countries with higher survival rates than the SES models and countries with lower. The individual comparisons are far more telling.

FIGURE 57: HFS MODELS AND WORLD´S DEMOGRAPHIC PYRAMIDS, 2005-2010.


VI. ECONOMIC EQUITY TO ENABLE GLOBAL HEALTH EQUITY AND THE UNIVERSAL RIGHT TO HEALTH

WORLD´S NATIONAL AVERAGE GDP PC DISTRIBUTION The world´s GDP pc by countries is distributed in a very uneven way, as the graph below shows. When distributed from lowest to highest GDP pc, it reflects an exponential curve, rather than a linear one56.

FIGURE 58: DISTRIBUTION OF GDP PC BY COUNTRY AVERAGES, 2013. The mean and coefficient of variation (standard deviation/mean) of the GDP pc according to the population weights are represented in the following graphs.

FIGURE 59: WORLD´S GDP PC BY POPULATION FREQUENCY OF NATIONAL GDP PC. The above graph reflects how the world´s average GDP pc has been increasing with a growing increase rate which seems to have reached plateaus in the 70s and in the 90s, possibly reaching now another plateau in the second decade of the XXIst Century, with the latest world economic crisis due to unregulated speculation. 56 http://data.worldbank.org/


FIGURE 60: VARIATION COEFFICIENT OF POPULATION-WEIGHTED NATIONAL AVERAGE GDP PC, 1961-2013.

The former graph shows how the dispersion of the population distribution of national average GDP pc values was stable from the 60s to the 80s, then increased till the turn of the century and has since then decreased. The effect of China´s increased GDP pc may have had a strong influence in the described trend. Population by GDP pc levels When we attribute to each GDP pc interval its countries´ total population, the distribution of the world´s population according to GDP pc is shown in the following graph. The larger population in the intervals $1000-2000 and $6000-7000 pc is due to the average GDP pc values of India and China, with high population numbers. A breakdown by states and regions would smooth the curve.

FIGURE 61: DISTRIBUTION OF COUNTRIES POPULATIONS BY GDP PC, 2013. Even if smoothed, this distribution shows a normal skewed distribution towards values far below the average GDP pc (10565$ pc), reflected in the difference between that mean value and the median (5700$ pc): the GDP pc of the mid-point of the world´s population.


Total GDP by GDP pc levels When we attribute to each GDP pc interval its countries´ total GDP, the distribution of the world´s GDP according to GDP pc intervals is shown in the following graph. The larger GDP amounts in the intervals $ are due to the average GDP pc value of the US.

FIGURE 62: DISTRIBUTION OF COUNTRIES´ TOTAL GDP ACCORDING TO GDP PC INTERVALS, 2013.

Even if smoothed, this distribution shows a skewed normal distribution towards values far above the average GDP pc (10565$ pc), reflected in the difference between that mean value and the median (38600$ pc): the GDP pc of the mid-point of the total world´s GDP. World´s GDP Gini index The following graphs show the Gini graphs (comparing cumulative GDP -Y axis- in cumulative population -X axis- by countries from lower to upper GDP pc levels) and the divergence from an egalitarian distribution line57.

FIGURE 63: GINI INDEX OF GLOBAL GDP, 2012. 57 Gini C. Italian: Variabilità e mutabilità (Variability and Mutability). 1912.


FIGURE 64: TREND OF GINI COEFFICIENT INDEXES: AVERAGE NATIONAL VALUES AND INTERNATIONAL DISTRIBUTION.

The distribution of cumulative wealth in 2012 shows a very high Gini index (0.64: 64% deviation from egalitarian distribution). Such value, and in fact all since 1960, are much higher than the average of national Gini coefficients, in the region of 0.4, as shown in the previous graph. The trend over time shows that there has been some decline in that distribution, which in contrast with the stable standard deviation may mean that the effect of China´s growth in the last decade smoothed the distribution.

FIGURE 65: INEQUALITY OF INTERNATIONAL GINI COEFFICIENTS AND SKEWED RATE, 1961-2012.

The last graph shows the "inequality of inequality" measured by the standard deviation of the differences of cumulative GDP from cumulative egalitarian mode, as measured in Gini. It shows that the inequality has decreased, probably due to the China effect above mentioned and that it is skewed to a higher median-than-mean distribution, that is, more unequal in cumulative populations under the world´s average GDP pc. The analysis of both, the normal and the cumulative distribution, and the dispersion and symmetry rates shows that the distribution of national average GDP pc across world's countries reflects high levels of inequality and with skewed concentration of population in lower levels of GDP pc and wealth in the upper levels.


MINIMUM DIGNITY THRESHOLD The following analysis aims at estimating the minimum level of income -measured in GDP pc- required to enjoy the standard levels of health (14c HFS models). It is based on the following methodology: 1. Minimum dignity threshold -mDTh- (to enable best feasible-sustainable health): average GDP pc58 of

the 14 constant healthy-feasible-sustainable (HFS) models selected in chapter II.

2. Hoarding threshold -HoTh- (above which the hoarding effect prevents to those under the mDTh to increase their levels and enable best feasible health standards/right to health): world´s weighted average GDP pc + 1.96 (p<0.05) deviation standard (estimated from the difference mDTh-average GDP pc/1.96).

3. Estimation of population and their GDP deficit of countries living below the mDTh, population and excess GDP of countries living above the HoTh and those in between both thresholds ("equity zone").

4. Redistribution required enabling global health equity from countries above HoTh to countries below mDTh.

5. Comparison of the above analysis with the one which uses the lower limit of the 14 HFS models ("the Asian efficient HFS").

6. Comparison of the required redistribution for GHE, with ODA. EQUITY (FAIR INEQUALITY) THRESHOLDS AND ETHICAL REDISTRIBUTION OF GDP

All quantitative variables in nature follow a normal distribution. Besides, an egalitarian world would not be socially and politically feasible (so history reveals). This is why the difference of equality and equity is important. Why do we measure inequality if we do not aim at equality? On the contrary, if we measure inequity (unfair difference rending many under the minimum dignity threshold disabling them from best feasible-sustainable health), we can aim at equity by redistribution which limits both ends: the lower below the dignity threshold, and the maximum above the hoarding threshold. If we would aim at having <2.5% of the world´s population with GDP pc lower than the mDTh (and mechanisms to detect and rescue them from that situation), we can estimate which proportion of the income above the hoarding level should be redistributed to those under the mDTh and later progress to the ideal ethical society where both ends are limited. 58 http://data.worldbank.org/


FIGURE 66: NORMAL EQUITY DISTRIBUTION TO ENABLE BEST FEASIBLE AND SUSTAINABLE HEALTH FOR ALL, REF AVERAGE HFS STANDARDS, 2012.

As the figure above shows, the minimum threshold in 2012 would be $5415 and the maximum $21,578.

FIGURE 67: TIME EVOLUTION OF MINIMUM, MEAN AND MAXIMUM THRESHOLDS OF EQUITY DISTRIBUTION OF RESOURCES TO ENABLE GLOBAL HEALTH EQUITY

AND THE UNIVERSAL RIGHT TO HEALTH, 1961-2012 (YEARS 1-53). The time evolution of the interval between both equity income thresholds has increased in magnitude (but much higher in the HoTh) and scope both, especially during the last decade.


Distribution of the world´s population in relation to the equity thresholds The following maps show the evolution of countries with GDP pc below the minimum dignity threshold -mDTh-, which would enable the best feasible and sustainable health standards. The maps show that in 1950-1955 only four countries in Latin America (Bolivia, Ecuador, Nicaragua, Honduras), most in sub-Saharan Africa (insufficient data) and in Asia Afghanistan, Pakistan, India, China, some South East Asia and Papua New Guinea, had income per capita below the average levels of the HFS models.

FIGURE 68: COUNTRIES WITH GDP PC BELOW THE MDTH, 1950-1955.

In 2005-2010 the map of countries below the mDTh changed with three in Central America (Guatemala, Nicaragua and Honduras), three in South America (Bolivia, Guyana and Paraguay -yet the latter in the HFS list-), most of Africa, except Libya and the south-western countries and Gabon, Ukraine, Iran, most "Stans", India, Bangladesh, Mongolia, some in South East Asia (including Vietnam, the lowest GDP pc from the 14c HFS models), Indonesia and Papua New Guinea.

FIGURE 69: COUNTRIES WITH GDP PC BELOW THE MDTH, 2005-2010.


FIGURE 70: DISTRIBUTION OF GDP REQUIRED TO ENABLE GLOBAL HEALTH EQUITY, BY COUNTRIES AND YEAR, 1960-2013.

FIGURE 71: DISTRIBUTION OF GDP REQUIRED TO ENABLE GLOBAL HEALTH EQUITY, BY SHARE OF COUNTRIES AND BY YEARS, 1961-2013.

The graphs above show the totals and share of GDP deficit under the mDTh. It shows how China held the highest gap until the mid-90s, when it surpassed by India; China was disappearing from the list by the end of the last decade. The total number of people living in those countries under the mDTh increased from over 1.5 Bn in 1960 to just under 3.5 Bn in 2012.


FIGURE 72: POPULATION LIVING IN COUNTRIES UNDER THE MDTH. The proportion of the world´s population living under the mDThH and unable to aim at best feasible health standards evolved from some 50% in the 60s, gradually increased to 70% from the mid-90s till 2009, when it dropped to again some 50% in the last years, due to the "graduation" of China from that group.

FIGURE 73: PROPORTION OF THE POPULATION LIVING IN COUNTRIES UNDER THE MDTH, 1961-2012.

The following graph shows in greater detail how China surpassed the level of GDP pc of the HFS models in relation to the trends of the world average and India. (China and India added some 40% of all the world´s population living under the mDThH until 2009; thereafter India hosts 25% of all).


FIGURE 74: TREND OF HFS COUNTRIES, THE WORLD´S AVERAGE, INDIA AND CHINA GDP PC.

The following graphs show the number and proportion of avoidable deaths due to GHiE over time. Between 80 and 90% of all avoidable deaths have taken place in countries below the mDTh.

FIGURE 75: AVOIDABLE DEATHS VS MDTHH.

DISTRIBUTION OF THE WORLD´S GDP IN RELATION TO THE EQUITY THRESHOLDS The following graphs show how the proportion of the world´s GDP enjoyed by the population living in countries with GDP pc below the mDTh has been in the range of 10% since 1960. The increase (10 to 15%) during 2000-2009 and fall after 2009 reflects the gradual increase of GDP of China and its further passage to the group of countries above the mDTh.


FIGURE 76: WORLD´S GDP BY COUNTRIES BY GDP VS MDTH. The charts below show the relation between the proportion of the world´s population living under the minimum dignity threshold and the GDP they share: 50-70% of the world´s population has been sharing 10-15% of the GDP, with the price in avoidable deaths (some 14.3 million in 2012, some 85% of all) due to global health inequity.

FIGURE 77: RELATION OF POPULATION AND GDP VS MDT.

As there is enough food and water for three times the present world´s population, there are also enough resources to enable universal conditions for best feasible and sustainable health levels. The chart below shows the time distribution of what would be the basic income if the entire world had the 14cHFS average GDP pc, and the excess GDP according to that basic need. As we will also in coming chapters, such excess GDP is concentrated in a small proportion of the population, prevents the ethical redistribution that is required, is correlated with natural exhaustion and does not translate in improved wellbeing nor in better health.


FIGURE 78: TIME DISTRIBUTION -1960-2013- OF WORLD´S GDP ACCORDING TO BASIC MDT AND EXCESS.

The trend of the share of the above mentioned world´s GDP required to enable best feasible and sustainable health for all has been in the range of 40-60% in the last 50 years.

FIGURE 79: PROPORTION OF WORLD´S GDP AS BASIC FOR HFS HEALTH STANDARDS AND EXCESS.

The graphs below show how the hoarding group of countries accumulated a large share of the world´s wealth, although that share has been decreasing (from 70 to 55%) since the mid-90s. Again, the China effect may explain much of this change due to its GDP growth, yet under the maximum threshold.


FIGURE 80: TREND AND SHARE OF HOARDING GDP.

The population living in hoarding countries has remained quite stable, around 1 Bn, while its share decreased from close to 40% in 1960, to some 15% in 2012, showing a progressive concentration of wealth and power in a shrinking part of the world, despite the decreasing GDP share before described.

FIGURE 81: POPULATION FROM HOARDING COUNTRIES.


WORLD´S POPULATION AND GDP IN THE "EQUITY ZONE" The distribution of the population and its GDP can be further disaggregated into those under the minimum (dignity) threshold -mDth-, those over the maximum (hoarding) threshold and those in between, in what we call, "the equity zone". The ethical redistribution should be from the hoarding excess to the one under the minimum threshold, so as to expand the equity zone. The graphs below show those distributions.

FIGURE 82: POPULATION BY GHE ECONOMIC THRESHOLDS. The above graphs show how most of the world´s population has been living in the extremes of hoarding (from 35 to 15%) or under-dignity levels (from 50 to 70%) with only some 10-15% in the middle ("equity zone") until 2009, when China and others joined that area and the proportion went up to almost 35%.

FIGURE 83: WORLD´S GDP BY GHE ECONOMIC THRESHOLDS.


The graphs above show how the world´s GDP has been concentrated in the excess above hoarding threshold (now 15% of the world´s population), which has kept on average two thirds of the world´s economic wealth. On the other extreme, the population living under the minimum level of dignity (over half the world´s population) enjoys only 10% of the world´s wealth. In between, only 20% of the world´s GDP was in the "equity zone", while it increased to one third after 2009, due to China joining that region. The graph below shows the ratio between minimum and maximum thresholds in the last 50 years. It reflects that the ratio (as explained above) was in the range of 6-7 from the mid-80s until China joined the equity zone in 2009 and such ration dropped to the levels of the early 60s.

FIGURE 84: RATIO OF MAX/MIN GDP PC THRESHOLDS FOR GHE, 1961-2013.

ETHICAL GDP REDISTRIBUTION REQUIRED TO ENABLE GLOBAL HEALTH EQUITY The following graphs show the ethical redistribution required to shift from the present skewed distribution of population according to GDP pc to a normal distribution -as most variables show in nature-, with over 95% of the population living in the "equity zone" and limiting both extremes under the minimum (dignity) and maximum (hoarding) thresholds.

FIGURE 85: ETHICAL SHIFT FROM PRESENT INEQUITY MODEL (16M AVOIDABLE DEATHS), TO EQUITY MODEL ENABLING THE UNIVERSAL RIGHT TO HEALTH.


Such shift would require the transfer of GDP, presently skewed towards the countries with GDP pc above the hoarding threshold, to the lower end. The graph below shows the combination of both the world´s skewed distributions (population and resources), largely outside the equity zone, and transition (through social and fiscal policies mainly) the normal (ethical and compatible with global health equity) distribution of population and GDP in the "equity zone" and the moderate difference between both, allowing for "fair inequality": equity.

FIGURE 86: SHIFT FROM PRESENT INEQUITY POPULATION AND RESOURCE DISTRIBUTIONS TO ETHICAL EQUITABLE DISTRIBUTION TO ENABLE

GLOBAL HEALTH EQUITY, 2013. The following graph reflects how, if conditions (economic equity as described above, among others) would allow, life expectancy would shift to the (fair) limits of global health equity, based on the standard healthy-feasible-sustainable (HFS) models (lower -Tonga- and upper -Costa Rica- limits for 2012), which would already translate in preventing over 14 million avoidable deaths under the mDTh, and further progress towards the best feasible health (lower threshold p<0.05 of HFS standards, upper threshold: present top 2.5% LE), if the countries in the upper end would gradually abolish their hoarding/exhausting effects. This would be the pertinent direction of science.


FIGURE 87: SHIFT FORM PRESENT GLOBAL HEALTH INEQUITY TO GLOBAL HEALTH EQUITY, REF. LIFE EXPECTANCY, 2012.

The following graph shows the redistribution required of carbon emissions so as to allow intergenerational (sustainable) health equity.

FIGURE 88: REQUIRED SHIFT FROM PRESENT WORLD´S POPULATION DISTRIBUTION, ACCORDING TO NATIONAL AVERAGE CARBON EMISSIONS TO A NORMAL

AND SUSTAINABLE DISTRIBUTION, 2012.


REDISTRIBUTION FROM EXCESS (HOARDING) COUNTRIES TO DEFICIT (BELOW MDTH THRESHOLD) COUNTRIES The maps below show how that GDP pc according to hoarding levels have been distributed in the world: they show those countries with deficit (below the mDTh), with excess GDP (above the MHoTh) and those in the equity zone (light green) in 1960, 1990 and 2010.

FIGURE 89: COUNTRIES BY HOARDING, EQUITY ZONE WORLD GDP AVERAGE AND < MINIMUM DIGNITY THRESHOLD, 1960.

FIGURE 90: COUNTRIES BY HOARDING, EQUITY ZONE WORLD GDP AVERAGE AND MINIMUM THRESHOLD, 1990.

FIGURE 91: COUNTRIES BY HOARDING, EQUITY ZONE WORLD GDP AVERAGE AND MINIMUM THRESHOLD 2012.


These maps reflect that in 1960 (yet with many countries missing comparable data) the countries under MDTh were Brazil, Bolivia, Ecuador, Honduras, Nicaragua and El Salvador in Latin America, most of Africa, the Middle-East and Asia, except Japan; those in the equity zone were in Mexico, Andean countries and Southern cone of South America, South Africa and Zimbabwe, the Iberian Peninsula, Turkey and Japan, while the hoarding countries were US and Canada, Central Europe and Scandinavia, Australia and New Zealand. Towards 1990 that distribution gradually changed, so that the countries under mDTh were in Central America and the Andean region, sub-Saharan Africa and Egypt, parts of the Middle East and still all Asia, except Japan and South Korea, the equity zone included the rest of Latin America, parts of Northern Africa, Gabon, Saudi Arabia, the ex-soviet republics and South Korea with the hoarding countries expanding to US, Canada, all Western Europe, Japan, Australia and New Zealand. In the last registers of 2012, the countries under the mDTh are Honduras, Guatemala, Salvador, Bolivia, Paraguay and Guyana in Latin America, all of Africa, except the south-west region and Lybia, some countries in the Balkans and Eastern Europe, some "Stans", India, Mongolia and South Asia; the equity zone includes the rest of Latin America, the <mDTh exceptions mentioned in Africa, most of the Middle East, the northern "Stans" and China, with the hoarding region as in 1990, but including now South Arabia, Arab emirates and South Korea. The net and proportional distribution and trend of hoarding countries´ GDP required for ethical redistribution enabling GHE is represented in the following graphs.

FIGURE 92: TREND OF GDP REQUIRED FOR ETHICAL REDISTRIBUTION BY HOARDING COUNTRIES, 1961- 2012.


FIGURE 93: TREND OF THE SHARE OF EACH HOARDING COUNTRY OF THE TOTAL REDISTRIBUTION REQUIRED, 1961-2012.

The graphs show how one third of the redistribution would come from the US, one third from Western Europe (mainly Germany, France, UK, Italy, Spain, the Netherlands, and Sweden) and the rest among Japan, Canada, Korea, Saudi Arabia, Switzerland, Norway and others. The trend of this distribution over the years shows an irregular increase by the US, with Japan and the western EU countries with more stable levels since the turn of the century, when the Arab rich states joined and gradually increased their share. The share of the total redistribution shows a gradual decrease by the US (from 60 to less than 40%), increase and decrease by Japan over the period, and more stable by others. REDISTRIBUTION LEVELS IF LOWER LIMITS OF HFS MODELS ARE TAKEN AS MINIMUM DIGNITY THRESHOLD The distribution required to limit to <2.5% of the world´s population to live with GDP pc lower than the lower limits (average of Vietnam and Sri Lanka), would be as follows:

FIGURE 94: NORMAL EQUITY DISTRIBUTION TO ENABLE BEST FEASIBLE AND SUSTAINABLE HEALTH FOR ALL, REFERRED TO LOWER LIMIT OF

BEST-FEASIBLE-SUSTAINABLE STANDARDS.


The table below summarizes the main differences between the thresholds and redistribution required when we take either the average GDP pc level of the healthy-feasible-sustainable (HFS) models or their lower limits59.

TABLE 7: DIFFERENCES OF FEATURES BETWEEN AVERAGE AND LOWER LIMITS OF BEST-FEASIBLE-SUSTAINABLE STANDARDS FOR GLOBAL HEALTH EQUITY

Features Lower equity threshold from average HFS

Lower equity threshold from lower limit HFS

Lower thr $5.418 $2.595

Upper thr $21.579 $24.583

% Pop <Lthr 48,33% (93 countries) 35,32% (56 countries)

%Pop >Uthr 14,33% (36 countries) 16,07% (32 countries)

%Pop EZ 47,34% (57 countries) 51,39% (98 countries)

% GDP <Lthr 8,81% 4,32%

%GDP >Uthr 61,75% 61,24%

%GDP EZ 29,44% 34,44%

Deficit $12,23 Tn $3,36 Tn

Excess $23,34 Tn $45,36 Tn

Deficit as % global GDP 16,54% 4,55%

Deficit as % excess GDP 52,40% 7,43%

Avoidable deaths in countries < LTh 12.822.141 9.908.061

Life years lost in countries < LTh 513.945.202 408.956.711

Cost per prevented avoid death $953.813 $339.599

Cost per prevented LYL $23.796 $8.228

ODA as % of deficit 1,099% 3,99%

The table below shows the percentage of GDP (and GDP pc) which would be required from the "hoarding countries" as ethical redistribution to prevent the global health inequity avoidable deaths. In bold letters those not yet members of OECD-DAC. 59 The average of Vietnam and Sri Lanka, in the lower end of the GDP pc distribution of the 14 HFS models: Asian efficient HFS models.


TABLE 8: PERCENTAGE OF GDP FOR ETHICAL REDISTRIBUTION FOR GLOBAL HEALTH EQUITY

Donor country % GDP

Lower limit HFS threshold Average HFS threshold

Luxembourg 11,59% 42,14%

Norway 11,31% 41,09%

Qatar 11,06% 40,19%

Macao SAR, China 10,98% 39,92%

Switzerland 10,52% 38,23%

Australia 9,77% 35,50%

Denmark 9,09% 33,04%

Sweden 9,03% 32,83%

Singapore 8,73% 31,74%

United States 8,51% 30,95%

Canada 8,38% 30,46%

Austria 8,02% 29,16%

Netherlands 7,83% 28,46%

Ireland 7,80% 28,35%

Finland 7,77% 28,25%

Belgium 7,51% 27,28%

Iceland 7,49% 27,21%

Germany 7,46% 27,11%

New Zealand 6,87% 24,96%

France 6,84% 24,87%

United Kingdom 6,44% 23,42%

Brunei Darussalam 6,28% 22,83%

Japan 6,27% 22,78%

Hong Kong SAR, China 6,19% 22,49%

Israel 5,74% 20,86%

Italy 5,36% 19,47%

Spain 3,64% 13,24%

Puerto Rico 3,42% 12,43%

Korea, Rep. 2,34% 8,51%

Saudi Arabia 2,28% 8,29%

Cyprus 1,99% 7,24%

Bahrain 1,67% 6,07%

Malta 0,65% 2,35%

Slovenia 0,62% 2,24%

Oman 0,00% 0,99%

Greece 0,00% 0,36%

In bold letters those not yet members of OECD-DAC.


COMPARATIVE ANALYSIS OF ODA WITH REDISTRIBUTION REQUIRED FOR GLOBAL HEALTH EQUITY In relation to the percentage of ethical redistribution required for GHE, analyzed in the previous chapter, the level of ODA60 is represented in the following graphs.

FIGURE 95: TREND OF GDP DEFICIT (MIN AND AV SCENARIOS) FOR GHE AND ODA LEVELS (OECD/DAC), 1961-2013.

FIGURE 96: PERCENTAGE OF DAC ODA OF DEFICIT (AVERAGE HFS THRESHOLD) FOR GLOBAL HEALTH EQUITY, 1961-2013.

The above graphs reflect the fact that the levels of ODA are only a small (and decreasing) proportion of the redistribution required to enable global health equity and the universal right to health. Even if the levels of ODA were more adequate to the challenge of health equity, the distribution of ODA per recipient country does not correlate with their needs. 60 http://www.oecd.org/dac/stats/


The following graph represents for 2013 the distribution of ODA pc in relation to the GDP deficit pc to enable global health equity.

FIGURE 97: CORRELATION BETWEEN ODA PER CAPITA RECEIVED (OECD/DAC) (X AXIS) AND GDP PC DEFICIT FOR GHE (Y AXIS).

Specifically, the above graph shows that there is no correlation between ODA and GDP deficit (or levels of GDP). The table below shows the major "deflactors" of such correlation, on both ends, "donor darlings" and "donor orphans", with mainly political reasons and a tendency to reduce ODA per capita in the countries with larger population and viceversa.

TABLE 9: DONOR DARLINGS AND DONOR ORPHANS IN ODA, 2012

"Donor orphans " 2012 ODA % deficit "Donor darlings" 2012 ODA % deficit

Indonesia 0.01% Fiji 7.10% India 0.03% Bosnia 8.35%

Korea, Dem. Rep. 0.07% Kosovo 9.01% Myanmar 0.18% Jordan 11.88%

Bangladesh 0.25% Solomon Islands 12.43% Madagascar 0.29% Vanuatu 12.58%

Eritrea 0.38% West Bank and Gaza 12.78% Algeria 0.41% Kiribati 14.08% Nepal 0.50% Serbia 15.35%

Guinea 0.51% Timor-Leste 16.81% Sudan 0.54% Namibia 18.53%

Cameroon 0.54% Cabo Verde 20.31% Ethiopia 0.61% Samoa 23.72%

Togo 0.64% Tonga 39.85% Guatemala 0.67% Marshall Islands 57.21%


Such almost absent correlation between needs according to GDP deficit and levels of ODA per capita has remained quite stable during the last 40 years, as the graph below shows.

FIGURE 98: CORRELATION OF ODA WITH GHE NEEDS, 1970-2012.

Considering also the Paris aid effectiveness commitments and the level of compliance by ODA "donors", the following table shows the main differences of the present framework of redistribution (ODA) and the required one for effective global health equity and the universal right to health.

TABLE 10: FEATURES OF ODA AND OF REDISTRIBUTION REQUIRED FOR GLOBAL HEALTH EQUITY

Feature ODA Redistribution x GHE

Donors OECD /DAC. Excess threshold (2012: > $21750 pc).

Recipients Developing countries (World Bank).

Minimum dignity threshold (2012: $5418 AvTh $2595).

Magnitude 0,29% of GDP (2013) (target 0.7%).

16.54 % (av thr 2013) and 4.55% (min threshold) of global GDP, and 52.40% (average thr 2013) and 7.53% (min threshold) of excess GDP.

Distribution Non-correlated with GDP deficit.

Correlated with GDP deficit for mDTh.

Predictability Low (1-2 years). Stable and adjusted to GDP evolution.

Binding nature Voluntary (volatile). Binding global mechanism.

Ownership Weak. Strong + global monitoring system.


VII. NATIONAL HEALTH EQUITY

AVOIDABLE DEATHS BY GLOBAL HEALTH EQUITY, DUE TO NATIONAL INEFFICIENCY/ INEQUITY While most avoidable deaths take place in countries with national average GDP pc below the threshold of income of the healthy-feasible-sustainable (HFS) models, a significant number take place in countries with higher income than those standards. The following pie chart details the number of avoidable deaths in 2005-2010 in countries with GDP pc above the mDTh (that is, avoidable deaths due to national inefficiency or other specific health challenges in those countries). It shows how Russia hosts over one third of those deaths, with South Africa and Brazil together another third and the rest distributed in the remaining countries of this group.

FIGURE 99: DISTRIBUTION OF AVOIDABLE DEATHS AMONG COUNTRIES WITH GDP PC ABOVE THE MDT.

The following graph represents the evolution of the number of avoidable deaths in the three countries with GDP pc above mDTh and higher number of avoidable deaths (two thirds of all avoidable deaths in this group of countries live in those three countries). The dotted line represents the periods when those countries had GDP pc lower than the mDTh. It shows an increase in Russia since the mid-80s, before the collapse of the Soviet Union, with a decrease after the turn of the century, when the GDP pc increased and surpassed the mDTh. It also shows an increase in South Africa since the 90s, due to the AIDS pandemic, and slowly reaching a plateau by the end of the first decade of the XXIst Century. Brazil has had a more stable number of avoidable deaths, slowly decreasing since year 2000, possibly in relation to the reduction of poverty levels during Lula´s government.


FIGURE 100: TREND OF AVOIDABLE DEATHS BY GHE IN MAIN COUNTRIES WITH GDP PC ABOVE THE MDT, 1960-2010.

But even if countries on average may not reveal burden of global health inequity in avoidable deaths or life years lost, their internal distribution of health may host national inequities which if added -as done in the overall burden of global health inequity- indicate the burden of national health inequity, a critical gauge of national and sub-national social cohesion and justice. Despite the WHO founding objective since 1945 and the call (and commitment) by all countries to report on national health equity, there are no international data or registers that do so. Below we mention the partial (in scope and coverage) attempts to measure national health inequalities through UNDP and WHO. LIFE EXPECTANCY ADJUSTED TO INEQUALITY The Human Development Report has introduced since 2011 the adjustment of the human development index to the inequality within countries61. For life expectancy this is done by comparing the arithmetic mean with the geometric mean and deducting the difference. For 2013, the graph below shows the correlation between HDI life expectancy at birth (Y axis) and HDI life expectancy at birth adjusted to inequality. It shows a linear strong correlation with minor variations on the proportional reduction of life expectancy, adjusted to inequality by the method above mentioned.

61 http://hdr.undp.org/en/faq-page/inequality-adjusted-human-development-index-ihdi


FIGURE 101: CORRELATION BETWEEN LIFE EXPECTANCY AND INEQUALITY ADJUSTED LIFE EXPECTANCY.

Surprisingly the loss of life expectancy at birth, as the graph below shows, is not correlated with the Gini coefficient.

FIGURE 102: CORRELATION BETWEEN LIFE EXPECTANCY LOSS AND GINI COEFFICIENT, 2013.

HEALTH EQUITY MONITOR -WHO- Since the call by the World Health Assembly in 2010 to countries to report on national health equity, WHO established the health equity monitor62. Such database measures differences or ratios between sub-groups by variables influencing health (rural/urban, level of education of the mother, income quintile) in some of their health indicators related to coverage of health services (immunization, skilled attended deliveries) or outcomes (under-five mortality). 62 http://www.who.int/gho/health_equity/en/


Only one third of countries have reported national health equity data to this framework. In fact, rather than official reporting, all data come from DHS and MICS surveys. The majority of data is incomplete, of questionable reliability and come from low income countries with only a few low-middle income countries. If in these countries we were to set as standard the health of those in the upper quintile, we may estimate the burden of health inequity for the only health outcome reported under-five mortality. For 2012 we could only estimate the burden of national health inequity in 7 countries and compare with the burden of global health inequity in avoidable deaths in under-5s. The graph below shows the net and relative share of the global vs national burden of health inequity.

FIGURE 103: GLOBAL VS NATIONAL NET (EXCESS <5 MORTALITY RATE) HEALTH INEQUITIES, 2012.

FIGURE 104: GLOBAL VS NATIONAL SHARE OF HEALTH INEQUITY, 2007-2012.


The graphs above show that, with the very limited data and assuming upper quintile as healthy-feasible-sustainable (HFS) models, the burden of national health inequity measured in avoidable deaths under 5 shows a share of the overall inequity (compared to global healthy-feasible-sustainable (HFS) models), which varies from countries where the intranational vector of inequity is close to half of the overall burden, especially middle income countries (Peru, Ukraine, Jordan), to countries where over 90% of the burden of inequity can be attributed to global inequity (mainly low income countries like Zambia, Swaziland, Kenya and Malawi), in which, even with perfect national health equity, a large share of deaths would be avoidable if there was an ethical redistribution of global resources. FRAMEWORK FOR INTERNATIONAL REPORTING ON NATIONAL HEALTH EQUITY

As the following table shows, when we compare the present system of health equity surveillance with the features national and international systems that should have to really measure and act on health inequities, we identify many weaknesses and a clear challenge for the international community.

TABLE 11: DIFFERENCES BETWEEN THE PRESENT AND LOGICAL FRAMEWORK TO INFORM ON

NATIONAL AND INTERNATIONAL HEALTH INEQUITY.

Features Present international

frameworks for national health equity

Standards of national health equity monitoring systems

Countries reporting 70 (low and low-middle income)

to health-equity monitor (incomplete reports).

All.

Measures

Inequality (ratios by stratifying variables-income, education, rural/urban) of health services (immunization, skilled delivery

attendance) and outcomes (under-5 mortality).

Inequity: best national health-feasible-sustainable standards. Burden of inequity by avoidable deaths and life and healthy life

years lost.

Source of data Demographic Health Surveys.

National regular monitoring system based on national registers at municipal level (<5000 pop size) (income,

carbon footprint, life expectancy, mortality by age groups and sex,

risk factors and diseases.

Utility

Arbitrary and mitigating measures to buffer the situation of access to services in the less-

healthy groups.

Minimum dignity conditions to enable national health equity through NHE as social inclusion

barometer and link to transformational policies (e.g. fiscal redistribution, territorial cohesion, access to services based on universal right to

health).

Impact Mitigation. Transformation.

Rationale Charity. Justice.


VIII. HOLISTIC HEALTH INDEX

The previous sections prove the major global challenge of health equity, from what we know of international average data and from what we don´t of national equity analysis. The analysis confirms the ethical principle described in chapter I, based on individual and collective commitment to the universal right to health. The following holistic health index aims at including the individual dimensions of our health and the effects we have on others through the hoarding and exhausting of economic and natural resources. It goes beyond, therefore, the individual (ego) and human (anthropo) centric approach to health. METHODOLOGY The estimate of the holistic health index includes five dimensions: 1. Estimate of the life expectancy at birth. Data of World Health Statistics. 2. Estimate of how much of the life expectancy at birth is enjoyed without disability (healthy life

expectancy). Data of Global Burden of Disease63. 3. Estimate of how many of the life year’s expectancy at birth, is lived with happiness/self-

satisfaction/sense of fullness of our physical and psychosocial (and spiritual) potential. Data of the World Happiness Report64.

4. Estimate of the negative impact on others through the hoarding effect. Here we calculate how much

of the national average GDP pc in excess of the maximum (hoarding) threshold is related to the loss of healthy life years of countries with national average GDP pc below the minimum dignity threshold.

5. Estimate of the negative impact on others through the exhausting effect. Here we calculate how

much of the carbon footprint (CO2 emissions) in excess of the planetary boundary is related to the loss of healthy life years in coming generations (models of health loss by 2015 with 3-5 ºC increase).

ESTIMATES OF HOARDING AND EXHAUSTING NEGATIVE IMPACT ON OTHERS HEALTH The following table explains the method of estimating of the effects of hoarding and exhausting per capita on life years lost of others. 63 http://www.who.int/topics/global_burden_of_disease/en/ 64 http://unsdsn.org/resources/publications/world-happiness-report-2013/


TABLE 12: EFFECTS OF HOARDING AND EXHAUSTING PC ON LIFE YEARS LOST

Negative dimension Threshold Population

Hoarding and exhausting

estimated excess levels 2012

LLY lost Impact per

hoarding and exhausting unit

Hoarding

> 21.750$ pc (average + standard

deviation of mDTh

enabling GHE).

1162 million from 36

countries.

2.33 Tn above max: 1.254 Tn of

unmet deficit: 52% of excess.

729 million LYLost due to

GHiE in countries < mDTh.

0.67 LYLost/year for each 1000 US $

above max. threshold.

Multiplied by each national life

expectancy to estimate to life-

hoarding negative impact.

Exhausting

> 2.5 mTns pc on average 2000-2010 (Planetary boundary).

3425 million from 97

countries.

14 Bn CO2 mTons above planetary limit.

10 million DALYs and 0.25 million

climate- change-

related (WHO 2050 scenario of 3 ºC above 1990 levels65).

0.05 Life year lost per each CO2 mT pc

emission above 2.5/year.

Multiplied by each national

life expectancy to estimate to life-hoarding

negative impact.

It shows that for each 1000 $ GDP pc above the maximum threshold (in 2012 was $21,750), 0.67 life years (250 days) in the population living below the minimum dignity threshold and in vital (and ethical) need of redistribution of (part of) the excess, are lost. The same estimate for the exhausting effect is based on a projection by WHO whereby 10 millions life years would be lost annually in a scenario of 3 ºC above the 1990 levels. This modeling may under-estimate the real impact of climate change, which may also be -at the present trend- higher than 3º C warming. RELATION BETWEEN HOARDING AND EXHAUSTING AND THE QUESTIONING OF ECONOMIC GROWTH BEYOND THE HOARDING THRESHOLD The two main root causes of global health inequity and loss of human life are (as the following analysis will show) hoarding and exhausting of resources from our Mother Earth (direct as using nature’s means and indirect through the power to do so: money). Both dynamics are interconnected by the fact that when we more produce and consume (and generate income, defined as the flow of money through transactions of goods and growingly: speculation-money), we spread more the income distribution curve and feed both extremes and we exhaust more the nature. The following graph shows that relation. 65 http://www.who.int/globalchange/summary/en/index6.html


FIGURE 105: CORRELATION BETWEEN HOARDING EFFECT ON LIFE YEARS AND CARBON EMISSIONS PER CAPITA, 2010.

The graph shows how the more negative (on life years) is the impact of the hoarding, more higher are rates of carbon emissions. The outliers are mainly the green bubbles for Arab oil producers, with higher exhausting than hoarding (less speculative) effects, and the orange bubbles for more speculative economies (OECD). The major red (China) and blue (India) bubbles are ready to increase and join this dangerous line of hoarding and exhaustion (China already starting that path), which dramatically seem to be for now the only political and economic understanding of human progress (self-destructive). Most interestingly, as seen in previous sections, there is no correlation between hoarding and life expectancy, as the graph below shows.

FIGURE 106: CORRELATION BETWEEN HOARDING AND LIFE EXPECTANCY, 2010. It reveals that while the non hoarding countries have as a group a lower average life expectancy than the hoarding ones (see blue line), there are many no hoarding models with life expectancy in the average range of the hoarding countries, and the correlation of only the hoarding countries’ GDP (hoarding effect) excess levels (above $21,000 pc) with life expectancy shows a flat line.


This analysis proves that hoarding (accumulating income beyond a maximum level, some $ 21,000 pc for 2012) is NOT GOOD FOR OTHERS (causes indirectly over 14 million avoidable deaths), NOT GOOD FOR THE COMING GENERATIONS (correlated with nature’s exhaustion as the previous graph showed) AND NOT GOOD FOR OURSELVES (does not increase our life expectancy and neither other social wellbeing indicators66). The question is: why is economic growth (measured in income through money flows) the only political and economic strategy for human development? On the other side, the limitation of income below the hoarding threshold would not only enable redistribution of resources and preventing the majority of avoidable deaths in countries below the mDTh, but also shift the average CO2 emissions from present average 3,70 mT pc to 2,63 mT pc, very close to the planetary boundary of sustainability. DIMENSIONS OF THE HOLISTIC HEALTH INDEX The following maps show the geographical distribution, for 2012, of each of the dimensions of the holistic health index.

FIGURE 107: HEALTHY LIFE EXPECTANCY OF WOMEN, 2012.

The best levels of healthy life expectancy in women (over 64 years) are in US, Canada, Argentina, Chile, Western Europe, Thailand, Japan, South Korea, Australia and New Zealand. The country with best women’s healthy life expectancy is Japan (70 years). The worst levels, under 50 years of healthy life expectancy include Bolivia, sub-Saharan Africa except Southwest Africa, and Afghanistan.

66 http://www.equalitytrust.org.uk/resources/spirit-level-why-equality-better-everyone


FIGURE 108: HEALTHY LIFE EXPECTANCY OF MEN, 2012.

The best levels of healthy life expectancy in men (over 62 years) are in US, Canada, Western Europe, Japan, Australia and New Zealand. The country with best men’s healthy life expectancy is Japan (66.6 years). The worst levels, under 50 years of healthy life expectancy include sub-Saharan Africa except Southwest Africa and Afghanistan.

FIGURE 109: HAPPINESS INDEX. The level of happiness, as assessed in the world happiness report67, identifies higher levels of happiness in the Americas, mainly Canada, US, Mexico, Costa Rica, Panama, Venezuela, Colombia, Argentina Brazil and Chile, Central and Northern Europe, the rich Arab countries (Saudi Arabia, Qatar and United Arab Emirates), Turkmenistan, Japan and Australia. The highest level is in Denmark (7.7 out of 10). 67 http://www.who.int/globalchange/summary/en/index6.html


FIGURE 110: LIFE YEARS LOST OF OTHERS, PER CAPITA AND LIFE, DUE TO HOARDING EFFECT.

Following the methodology described above, the countries with GDP pc above the maximum threshold (hoarding) that indirectly decrease during lifetime over 40 years of LE in countries under the Minimum Dignity Threshold are Canada, US, Western Europe (except the Iberian Peninsula with slightly lower levels), Japan and Australia.

FIGURE 111: LIFE YEARS LOST OF OTHERS, PER CAPITA AND LIFE, DUE TO EXHAUSTING EFFECT.

The worst exhausting effects due to high levels of carbon emissions per capita are US, Canada, UK, Norway, Eastern Europe, Russia, Libya, Saudi Arabia, Japan and Australia.


HOLISTIC HEALTH INDEX

FIGURE 112: HOLISTIC HEALTH INDEX.

TABLE 13: COUNTRIES WITH HIGHER HOLISTIC HEALTH INDEX

Country Healthy life expectancy

Happiness index

LYLs x exhausting

LYLs x hoarding

Consolidated health index

Costa Rica 64.65 7.27 61.48

Panama 62.77 7.32 -0.07 60.24

Venezuela 61.92 7.48 -2.34 58.88

Mexico 61.80 6.80 -0.69 55.24

Chile 62.63 6.64 -0.96 54.54

Brazil 58.70 6.84 53.94

El Salvador 58.36 6.74 53.32

Argentina 62.25 6.44 -1.06 52.44

Colombia 60.09 6.41 51.70

Belize 60.42 6.45 51.10

The map and table above show how the best consolidated health indexes are in Latin America, with Costa Rica ranking first. Only three out of the 14 feasible-sustainable models (Costa Rica, Colombia and Belize) feature among the top ten. The only non-Latin American country in the best 14 countries’ holistic indexes is Thailand. Countries from southern (Greece, Malta) and Central Europe (Slovenia and Slovakia) follow.


The next graphs show how the different dimensions of the health index compare with the best potential healthy-happy and collective dimensions of life expectancy (at close to 70 years). Costa Rica, the best performer, with a minor loss due to premature death, disability and sadness and the world’s average level with loss of two thirds of the human potential by mainly sadness (one third of the loss), and also premature death, disability and hoarding effect.

FIGURE 113: POTENTIAL HEALTHY HAPPY AND HOLISTIC (COLLECTIVE) LIFE VALUE VS BEST PERFORMER AND WORLD´S AVERAGE.

FIGURE 114: HOLISTIC HEALTH INDEX IN THE STANDARD BEST FEASIBLE AND SUSTAINABLE HEALTH MODELS FOR GHE.


The analysis of the different health holistic indices of the ten (with available data) of the fourteen healthy-feasible-sustainable (HFS) countries shows how, while there is a moderate loss of life due to premature death and disability (here estimated against the best life expectancy level, Japan), the main factor influencing the differences on final holistic index is sadness. Countries like Albania, Cuba, Georgia, Sri Lanka and Vietnam have lower happiness indexes with a lower healthy happy life expectancy. Interestingly, those five countries have a communist history and/or present order and while it may have meant greater collective good and restrained the capitalist production/consumption machinery, the effect on individual freedom (even of being wrong) may have an effect in happiness, an essential element of holistic health.

FIGURE 115: HOLISTIC HEALTH INDEX IN 10 COUNTRIES WITH HIGHEST HDI VS BEST HHI AND POTENTIAL HHI.

The analysis of the HHI among the countries with higher Human Development Index as estimated by UNDP in 2013, in contrast with the best potential and best performer (Costa Rica), reveal that all of them have very low HHI, mainly due to a major negative impact of hoarding and, to a lesser extent (most likely under-estimated), exhausting. Interestingly, the effect of sadness is minor. Hoarding tends to go with isolated societies (living in their comfort shells) with reduced (and only volatile and mitigating charity) empathy (possibly even lacking information) for the pain their cause in others near and less so if distant. In this context, the message of ODA as able to bring on its own higher global justice is not only wrong but counter-productive. In fact, all except Canada, Singapore and New Zealand (those with a very low positive HHI) have negative levels of HHI, meaning that each person living in these “development model countries” prevents on average at least the life of another person in countries below the minimum threshold. This analysis questions the present concept, reference and framework of development, which does not relate or connect excess with the need and is also complacent with the production/consumption models exhausting our mother Earth and preventing our children and grandchildren to enjoy it.


IX. CONCLUSIONS AND POLITICAL PROPOSALS

CONCLUSIONS 1. The research described in this book is based on the ethical principles -and neglected international

commitments- of the universal right to health through health equity. 2. It identifies models of good health (on average with 10% higher life expectancy than the average)

which are replicable (use resources below the world’s average) and sustainable (do so with carbon emissions below the planetary boundary).

3. These healthy-feasible-sustainable (HFS) models allow us to estimate the burden (gap from

objective) of health inequity and reflect its distribution by countries, sex, age-groups and time in the last 60 years.

4. Health inequity may be the best indicator of socioeconomic inclusion (justice) within and between

generations. 5. It concludes that socioeconomic inclusion and justice (measured through the universal -and

equitable- right to health) has remained stagnant since the mid-80s (and possibly marked by the Washington consensus) and reveals that more than one in three deaths every year are avoidable, and affects more those in countries with lowest income, and also women and the younger.

6. It also enables the setting of an income “minimum dignity threshold” (not that people living below

have less dignity but that their dignity and right to live is not being respected) and the equitable distribution of the world’s resources which would enable the universal and equitable right to health.

7. By doing so, it also looks at nature (and how most variables follow a “normal” distribution) and

estimates the maximum threshold of income, above which the hording of resources has an effect in health inequity by preventing sufficient resources from reaching the basic health needs of others.

8. The world has enough natural and economic resources to enable the universal right to health, even

with a wide fair-inequality distribution of resources (allowing ratios between extremes of 4 to 7). 9. The levels of redistribution required -from the hoarding extreme to the under dignity- challenge the

present model of ODA in its magnitude, distribution, volatility and non-binding nature. 10. There is no international framework for the monitoring of the national levels and features of health

equity, and only low income countries report (through high-income countries’ financed surveys) on inequalities. Setting national standards is essential to address the subnational challenge and action of health equity and balance it with the burden of global health inequity requiring global actions.

11. The holistic health index (individual healthy happy and impact on collective life expectancy) reveals

that the world enjoys only one third of the potential healthy happy life expectancy respecting as well others.


PROPOSALS

The evidence above listed calls for transformational changes at international, national, social and individual levels: 1. International level

a. Adherence and compliance with the binding commitment (with reporting and accountability

mechanisms) of all nations to social, economic and cultural rights, complementing political and civil rights and freedom. (ICESCRs and its optional protocol).

b. Setting of global health feasible and sustainable standards, and monitoring frameworks (mapping its geographical -international and subnational- and social distribution) on global health inequity, as the barometer of national and international social inclusion/cohesion.

c. Agreement of an international framework of economic equity enabling the right to life and health, and setting the redistribution levels and flows from countries above the maximum threshold to those below the minimum in a sufficient and binding way.

d. Agreement on the international ethical and legal must to respect the sustainable limit on carbon emissions and planetary boundaries.

2. National level

a. Constitutional recognition of the universal right to health through a national binding commitment

(and reporting and accountability mechanisms) on social, economic and cultural rights, complementing civil right and freedom.

b. Setting of national health feasible and sustainable standards, and monitoring frameworks (mapping its geographical and social subnational distribution) of national health equity (and the burden of national health inequity) as the barometer of national social inclusion/cohesion.

c. Agreement on a national framework of economic equity (minimum levels of dignity and maximum levels of hoarding), enabling the right to life and health, and setting the redistribution (fiscal equity) levels and flows from social groups with income above the maximum threshold to those below the minimum level of dignity.

d. Agreement on the national legal and ethical must to limit carbon emissions and surface use per capita below the planetary boundaries.

3. Social and individual level

a. Recognition on the fact that the universal right to health is feasible and an ethical must for all to

strive towards it in her or his capacities and social roles (for health workers a revised Hippocratic Oath promoting the universal right to health).

b. Promotion by all means and ways of social and individual awareness and compliance with the ethical must of respecting, at individual level, the planetary boundaries (exhausting threshold), and the effect it has on coming generations.

c. Recognition of the fact that the levels of income above the "hoarding threshold" have a negative effect on the lives and health of others (who live under the minimum threshold of dignity).


RESEARCH AGENDA This document describes an analysis of the burden of health equity, based on the health right and equity principles, at global level, based on international databases. Many questions are still open on the health equity challenges at national, subnational and community/individual levels. These are some of the open questions which need to be examined and surely will lead to more: 1. International level

Dynamics and blockages for the adherence to the ICESCRs and its optional protocol. Measurement and analysis of "by all, in all and for all" dimensions of global health initiatives. Validity of WB, UN and WHO/GBD data and their limitations. Validity of the above data in the 14 HFS models. Specificities of the 14 HFS models. Specificities of HFS efficient models. Update of data for the five-year period 2010-2015. Models of future trends in equity. Hoarding-deficit correlations. Hoarding-exhausting correlations. Models of health impact from climate change. Global fiscal structure and mechanisms to ensure GHE. Opportunities to address GHE ethical redistribution replacing traditional ODA. Inter-cultural validity of assessment of happiness indexes. Political strategies for monitoring and reporting of GHE as WHO main goal. Political strategies to introduce GHE as measurable objective in the post-2015 agenda. Pertinence and opportunities for an International Institute for Studies and Proposals on Health,

Social and Ecological equity (IISPHSEE). 2. National level

Availability of national mechanisms of guarantee/reporting on the national compliance of health as a universal human right.

Availability of health, income and carbon footprint data at municipal and sub-municipal levels. Specificities of the national HFS models. Specificities of national HFS efficient models. Update of data for the five-year period 2010-2015. Models of future trends in national equity. National hoarding-deficit correlations. National hoarding-exhausting correlations. National fiscal structure and mechanisms to ensure NHE. Models of health impact by national effects of deforestation, biodiversity, water exhaustion and

others. National inter-cultural validity of assessment of happiness indexes. Political strategies for monitoring and reporting of GHE as national health main goal. Political strategies to introduce GHE as measurable objective in the social inclusion national

policies. Pertinence and opportunities for a National Institute for Studies and Proposals on Health, Social

and Ecological Equity (NISPHSEE). Share and correlations of global and national burden of health inequity.


3. Community and individual levels

Awareness of health as a human right. Awareness of health professionals on health as a human right and its equity/sustainability

dimensions. Individual hoarding (and its impact of others´ health) monitoring mechanisms (apps and other user-

friendly appliances). Individual exhausting (and its impact of others´ health) monitoring mechanisms (apps and other

user- friendly appliances). Exercise of freedom and its correlation with the common good. Perceptions of happiness and potential happiness and its relation with the responsibility and

commitment to common good. Potential of physical and psychosocial fulfillment gap dynamics and its relations of holistic health. Correlations between physical and psychosocial fulfillment and ecological sustainability, and

knowledge/participatory community dynamics.


X. ANNEXES

ANNEX 1: BURDEN OF GLOBAL HEALTH INEQUITY IN LOSS OF LIFE EXPECTANCY

Country 1960 1970 1980 1990 2000 2010 2012

Afghanistan -29.46 -28.54 -27.64 -21.93 -17.37 -14.81 -14.21 Albania 1.21 1.73 1.36 1.46 2.06 2.57 2.63 Algeria -14.91 -14.85 -10.71 -3.74 -3.28 -3.79 -3.83 Angola -28.06 -28.18 -28.72 -29.36 -27.01 -23.75 -23.25 Antigua and Barbuda 0.74 0.33 -0.29 0.67 1.18 0.93 0.95 Argentina 4.17 1.39 0.64 1.05 1.53 1.26 1.30 Armenia 4.82 4.73 1.81 -2.75 -0.94 -0.19 -0.28 Aruba 4.53 3.89 3.34 2.89 1.51 0.54 0.49 Australia 9.78 5.82 5.46 6.50 7.02 7.29 7.38 Austria 7.54 4.69 3.55 5.03 5.81 5.97 6.22 Azerbaijan -0.21 -0.46 -4.20 -5.75 -5.46 -3.96 -4.09 Bahamas, The 1.69 0.67 -0.42 0.13 0.04 0.18 0.20 Bahrain -8.95 -1.50 0.90 2.01 2.38 1.86 1.82 Bangladesh -14.02 -17.61 -14.01 -10.49 -6.89 -4.92 -4.42 Barbados -0.28 -0.27 -0.65 0.38 0.81 0.39 0.42 Belarus 6.67 4.88 0.92 0.34 -3.30 -4.00 -2.65 Belgium 8.66 5.77 4.33 5.56 5.51 5.83 5.67 Belize -1.08 0.35 0.69 0.69 -1.67 -1.14 -1.02 Benin -23.76 -22.85 -21.64 -17.08 -17.03 -15.66 -15.60 Bhutan -28.68 -28.26 -23.92 -18.03 -11.92 -7.40 -6.83 Bolivia -18.37 -19.43 -16.91 -11.72 -9.26 -8.09 -7.79 Bosnia and Herzegovina -0.77 0.86 1.35 -3.05 2.46 1.40 1.41 Botswana -10.51 -10.59 -8.17 -7.56 -21.72 -27.97 -27.73 Brazil -6.35 -6.34 -6.17 -3.98 -1.96 -1.33 -1.10 Brunei Darussalam 1.42 2.08 1.99 3.22 3.82 3.58 3.66 Bulgaria 8.21 6.06 2.28 1.15 -0.55 -0.90 -0.40 Burkina Faso -26.56 -26.10 -22.88 -21.13 -21.73 -19.40 -18.85 Burundi -19.81 -21.42 -21.59 -23.17 -24.01 -21.78 -21.09 Cabo Verde -12.04 -13.04 -8.66 -4.61 -2.62 -0.55 -0.17 Cambodia -19.84 -23.52 -39.26 -15.74 -10.33 -3.77 -3.31 Cameroon -19.52 -19.12 -17.67 -16.90 -20.28 -20.71 -20.13 Canada 10.09 7.50 6.20 6.88 7.02 6.49 6.52 Caribbean small states 0.95 0.08 -1.22 -1.67 -2.88 -2.98 -2.92 Central African Republic -24.56 -23.26 -20.17 -24.45 -28.52 -26.31 -25.24 Chad -23.03 -23.89 -24.21 -24.15 -25.53 -24.64 -24.02 Chile -4.02 -3.19 0.18 3.05 4.54 4.64 4.86 China -17.58 -2.29 -1.85 -1.02 -0.07 0.48 0.48 Colombia -4.33 -4.33 -3.40 -2.20 -1.23 -1.04 -0.94 Comoros -17.60 -17.49 -16.92 -14.90 -14.33 -14.20 -14.07 Congo, Dem. Rep. -20.02 -21.35 -22.79 -23.06 -25.86 -25.42 -25.09


Country 1960 1970 1980 1990 2000 2010 2012

Congo, Rep. -12.46 -11.84 -12.74 -15.32 -19.91 -17.20 -16.42 Costa Rica 0.56 1.64 3.65 5.23 5.52 4.87 4.99 Cote d'Ivoire -24.17 -21.45 -18.17 -17.89 -25.76 -24.73 -24.31 Croatia 3.57 3.00 1.30 1.67 0.59 2.07 2.21 Cuba 2.86 4.68 4.95 4.15 4.46 4.31 4.35 Cyprus 8.55 7.32 5.88 6.02 5.76 4.90 4.92 Czech Republic 9.31 4.24 1.40 0.89 2.75 3.02 3.36 Denmark 11.13 8.15 5.22 4.31 4.38 4.69 5.34 Djibouti -17.02 -16.06 -15.26 -13.83 -15.20 -14.12 -13.41 Dominican Republic -9.20 -6.66 -5.84 -2.55 -1.58 -1.62 -1.48 Ecuador -7.92 -7.42 -5.95 -1.66 1.14 1.24 1.48 Egypt, Arab Rep. -13.03 -13.08 -10.56 -5.95 -3.62 -3.96 -3.81 El Salvador -9.74 -8.65 -12.11 -4.62 -2.66 -2.77 -2.61 Equatorial Guinea -24.31 -25.46 -25.97 -23.94 -24.45 -22.88 -22.10 Eritrea -23.68 -24.67 -25.56 -22.26 -16.19 -13.22 -12.48 Estonia 6.86 4.74 0.04 -1.02 -1.80 1.02 1.71 Ethiopia -22.64 -22.26 -25.13 -23.55 -19.97 -12.94 -11.75 Fiji -5.08 -5.29 -5.87 -4.92 -4.61 -5.03 -4.97 Finland 7.78 4.98 4.56 4.32 5.25 5.46 5.91 France 8.83 6.46 5.17 6.10 6.84 7.26 7.85 French Polynesia -4.68 -4.92 -4.17 -1.67 0.24 1.28 1.41 Gabon -21.48 -18.57 -14.03 -9.21 -12.53 -12.12 -11.64 Gambia, The -29.00 -27.33 -22.53 -18.28 -17.04 -16.27 -16.11 Georgia 2.39 1.96 0.50 -0.50 -0.58 -0.73 -0.77 Germany 8.27 5.44 3.80 4.68 5.71 5.58 6.18 Ghana -15.21 -15.86 -16.61 -13.74 -15.23 -13.81 -13.77 Greece 7.12 5.71 4.77 6.44 5.67 5.98 5.92 Grenada -1.23 -1.61 -2.51 -2.02 -1.96 -2.07 -2.11 Guatemala -15.51 -13.15 -11.68 -8.26 -4.47 -3.41 -3.05 Guinea -26.16 -28.55 -27.62 -20.58 -20.96 -19.11 -18.87 Guinea-Bissau -18.90 -21.16 -23.00 -21.42 -20.77 -20.85 -20.68 Guyana -2.81 -5.69 -8.20 -8.37 -8.73 -8.71 -8.67 Haiti -18.88 -18.01 -18.15 -16.07 -14.79 -12.54 -12.01 Honduras -14.75 -12.68 -9.27 -3.81 -1.73 -1.56 -1.22 Hong Kong SAR, China 5.96 6.25 5.79 6.88 8.66 8.57 8.76 Hungary 6.96 3.97 0.18 -1.18 -0.97 -0.20 0.35 Iceland 12.39 8.74 7.97 7.54 7.44 7.49 8.20 India -19.66 -16.36 -13.50 -11.97 -10.05 -8.71 -8.51 Indonesia -16.24 -12.81 -10.29 -7.05 -4.96 -4.24 -4.11 Iran, Islamic Rep. -16.10 -14.33 -14.21 -7.05 -2.61 -1.28 -0.95 Iraq -13.01 -7.01 -8.59 -2.09 -1.38 -5.58 -5.47 Ireland 8.65 5.80 3.67 4.25 4.32 6.34 6.18 Israel 6.02 5.00 6.11 6.74 7.19 6.99 Italy 8.08 6.36 5.06 6.48 7.56 7.93 8.22 Jamaica 3.15 2.99 1.61 0.16 -1.74 -1.56 -1.43 Japan 6.62 6.75 7.21 8.34 8.86 8.43 8.38


Country 1960 1970 1980 1990 2000 2010 2012

Jordan -8.36 -5.01 -2.65 -0.57 -0.43 -0.97 -0.97 Kazakhstan -2.67 -2.92 -2.25 -2.16 -6.70 -6.11 -5.11 Kenya -14.68 -12.97 -11.06 -11.38 -19.37 -14.86 -13.63 Kiribati -14.17 -13.82 -13.04 -9.86 -7.61 -6.52 -6.18 Korea, Dem. Rep. -9.96 -5.92 -3.13 -1.15 -7.23 -5.51 -5.22 Korea, Rep. -8.04 -3.16 -3.07 0.80 3.62 6.14 6.65 Kuwait -0.69 0.94 0.81 1.76 1.14 -0.25 -0.36 Kyrgyz Republic -4.91 -4.95 -5.97 -2.20 -3.66 -5.11 -4.71 Lao PDR -17.85 -18.97 -19.87 -16.38 -10.57 -7.51 -6.91 Latvia 8.74 4.64 -0.07 -1.22 -1.90 -0.93 -0.94 Lebanon 2.24 0.88 -0.92 -0.27 2.18 4.84 5.13 Lesotho -14.53 -16.29 -15.21 -11.16 -25.03 -26.92 -25.88 Liberia -26.26 -25.90 -22.86 -23.29 -19.80 -14.97 -14.51 Libya -18.40 -9.08 -4.59 -1.93 -0.25 0.38 0.46 Lithuania 8.81 5.61 1.60 0.67 -0.19 -1.14 -0.85 Luxembourg 7.40 4.79 3.20 4.51 5.66 6.22 6.68 Macao SAR, China 3.62 3.81 3.59 4.78 5.41 5.28 5.41 Macedonia, FYR -0.42 1.09 -0.23 0.65 1.03 0.31 0.32 Madagascar -21.07 -20.42 -19.85 -19.49 -13.74 -11.06 -10.47 Malawi -23.21 -24.59 -24.51 -23.38 -26.18 -20.94 -19.99 Malaysia -1.57 -0.74 -0.82 0.26 0.64 0.09 0.13 Maldives -25.18 -21.04 -16.62 -9.89 -2.75 2.38 2.86 Mali -32.83 -32.81 -29.27 -24.03 -23.16 -20.64 -20.11 Malta 6.56 5.24 4.05 4.68 5.99 6.99 6.03 Mauritania -17.56 -16.09 -14.64 -12.11 -12.54 -13.39 -13.37 Mauritius -2.30 -2.08 -1.91 -1.09 -0.55 -1.44 -1.15 Mexico -3.98 -3.85 -2.32 0.30 2.05 2.28 2.42 Micronesia, Fed. Sts. -3.46 -3.56 -3.69 -4.30 -4.94 -5.79 -5.87 Moldova 0.77 -0.39 -4.12 -3.03 -5.32 -5.95 -6.02 Mongolia -12.67 -9.85 -11.95 -10.23 -9.29 -7.51 -7.38 Montenegro 2.66 4.54 3.99 3.85 1.81 0.01 -0.07 Morocco -12.60 -12.66 -11.37 -5.81 -4.08 -4.24 -4.07 Mozambique -26.04 -25.98 -26.03 -26.92 -24.79 -25.27 -24.88 Myanmar -18.31 -14.23 -13.94 -11.80 -10.19 -9.83 -9.78 Namibia -14.16 -12.67 -11.20 -9.27 -17.09 -11.93 -10.83 Nepal -22.59 -22.77 -21.22 -15.46 -10.17 -7.30 -6.73 Netherlands 12.35 8.39 6.86 6.38 5.77 6.29 6.39 New Caledonia -2.40 -2.17 -2.31 -0.01 2.71 1.42 1.54 New Zealand 10.19 6.07 3.95 4.88 6.42 6.29 6.44 Nicaragua -14.04 -11.55 -10.38 -6.36 -2.57 -0.61 -0.25 Niger -25.53 -28.93 -29.47 -26.54 -21.51 -17.42 -16.75 Nigeria -23.86 -24.02 -23.33 -24.39 -25.59 -23.12 -22.61 North America 8.85 5.79 4.92 4.94 4.68 4.37 4.27 Norway 12.51 8.89 6.79 6.04 6.42 6.59 6.74 Oman -18.37 -14.94 -9.15 -3.23 0.14 1.64 1.88 Other small states -15.55 -15.25 -14.20 -11.50 -15.88 -15.68 -15.18


Country 1960 1970 1980 1990 2000 2010 2012

Pacific island small states -7.42 -7.18 -7.16 -6.80 -5.48 -5.14 -5.03 Pakistan -14.61 -11.70 -10.81 -9.31 -8.33 -8.28 -8.28 Panama -0.17 0.35 1.39 2.56 2.90 2.54 2.65 Papua New Guinea -22.58 -19.16 -15.99 -14.79 -13.41 -12.40 -12.42 Paraguay 2.75 0.24 -2.11 -2.48 -2.14 -2.38 -2.52 Peru -13.35 -11.74 -8.81 -4.97 -1.71 -0.50 -0.20 Philippines -3.23 -4.39 -6.72 -5.27 -5.42 -6.18 -6.16 Poland 6.64 4.67 1.22 0.39 1.53 1.84 2.08 Portugal 1.77 1.87 2.34 3.47 4.10 4.62 5.66 Puerto Rico 7.68 6.35 4.82 3.67 4.48 3.77 3.82 Qatar 0.21 3.33 3.97 4.80 4.61 3.74 3.74 Romania 4.60 2.86 0.21 -0.75 -1.05 -0.95 -0.15 Russian Federation 5.01 2.94 -1.84 -1.59 -6.87 -5.55 -4.26 Rwanda -18.79 -20.91 -20.97 -37.88 -24.57 -12.20 -11.22 Samoa -11.07 -10.23 -8.91 -5.46 -2.73 -2.00 -1.73 Sao Tome and Principe -10.59 -9.33 -8.37 -8.72 -8.92 -8.55 -8.58 Saudi Arabia -15.38 -12.47 -5.77 -1.35 0.40 0.67 0.78 Senegal -22.84 -25.98 -19.94 -13.31 -14.44 -11.57 -11.51 Serbia -0.08 -0.47 0.52 Sierra Leone -30.71 -30.59 -28.23 -33.14 -34.10 -29.57 -29.39 Singapore 4.62 3.08 3.16 5.53 5.84 7.13 7.43 Slovak Republic 8.93 4.94 1.53 0.44 0.84 0.70 1.39 Slovenia 7.94 3.41 2.23 2.71 3.20 5.01 5.41 Solomon Islands -11.66 -10.81 -9.99 -13.79 -9.38 -7.34 -7.21 Somalia -24.08 -24.25 -24.14 -25.11 -21.34 -20.38 -20.02 South Africa -12.01 -12.35 -11.91 -8.38 -16.38 -20.02 -18.62 South Sudan -29.37 -29.39 -29.84 -26.97 -22.98 -20.94 -20.07 Spain 8.07 6.83 6.47 6.34 6.75 7.22 7.66 Sri Lanka -1.29 -0.92 -0.57 -0.82 -1.06 -0.65 -0.65 St. Lucia -3.74 -2.35 -0.10 0.55 -0.79 0.00 -0.04 St. Vincent and the Grenadines -3.32 -0.36 -1.50 -0.42 -1.63 -2.22 -2.31 Sudan -12.85 -12.98 -14.64 -14.98 -14.24 -12.93 -12.85 Suriname -1.36 -1.92 -2.94 -2.99 -4.28 -4.07 -3.90 Swaziland -16.81 -17.24 -14.65 -11.15 -23.55 -26.06 -25.87 Sweden 11.96 9.45 6.86 7.04 7.43 7.04 6.99 Switzerland 10.27 7.82 6.58 6.75 7.47 7.84 7.98 Syrian Arab Republic -8.27 -6.03 -3.06 -0.24 1.12 0.46 -0.01 Tajikistan -4.89 -5.17 -6.64 -7.63 -8.44 -7.41 -7.46 Tanzania -17.39 -18.52 -18.39 -20.03 -22.24 -15.23 -13.87 Thailand -5.80 -5.67 -4.69 -0.08 -1.30 -0.59 -0.53 Timor-Leste -27.31 -25.67 -34.45 -22.02 -12.73 -8.46 -7.70 Togo -20.77 -18.66 -16.57 -14.65 -18.67 -18.94 -18.57 Tonga 0.32 -0.31 -1.33 -0.91 -1.45 -2.23 -2.23 Trinidad and Tobago 1.62 -0.09 -1.82 -2.47 -3.62 -4.81 -4.90 Tunisia -19.03 -14.06 -6.85 -0.19 0.39 0.19 0.38 Turkey -15.66 -12.94 -10.19 -6.21 -2.22 -0.20 0.15


Country 1960 1970 1980 1990 2000 2010 2012

Turkmenistan -6.58 -6.84 -8.02 -7.82 -8.31 -9.39 -9.40 Uganda -17.06 -16.39 -19.44 -23.00 -24.10 -17.11 -16.07 Ukraine 7.26 5.04 -0.06 -0.36 -4.35 -4.13 -3.77 United Arab Emirates -8.80 -3.30 -0.90 1.22 2.19 2.19 2.24 United Kingdom 10.08 6.77 4.80 5.38 5.53 5.99 6.78 United States 8.73 5.61 4.78 4.72 4.42 4.13 4.03 Uruguay 6.85 3.49 1.41 2.04 2.45 2.21 2.19 Uzbekistan -2.16 -2.42 -3.61 -3.81 -5.27 -6.55 -6.61 Vanuatu -14.55 -12.72 -10.34 -7.30 -4.66 -3.57 -3.31 Venezuela, RB -1.51 -0.20 -0.66 0.61 0.23 -0.24 -0.23 Vietnam -1.97 -5.46 -1.45 0.01 1.42 0.90 0.89 Virgin Islands (U.S.) 2.69 2.55 2.55 4.29 4.41 4.76 4.76 Yemen, Rep. -28.66 -24.04 -18.32 -12.73 -11.76 -11.88 -11.81 Zambia -15.93 -16.17 -17.73 -26.68 -30.43 -19.88 -17.69 Zimbabwe -9.50 -10.30 -9.50 -11.36 -28.29 -20.82 -16.67


ANNEX 2: BURDEN OF GLOBAL HEALTH INEQUITY IN AVOIDABLE DEATHS

Major area, region, country or area *

1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

Afghanistan 219743 220305 222948 237317 240998 207285 230362 163076 167342 187124 186797 174888

AFRICA 3883520 4054797 4219455 4516334 4738909 5119144 5376334 5830486 6758808 7535027 7907477 7767781

Albania 3595 2888 -368 -865 -936 -403 -975 -1438 351 32 -3207 -2894

Algeria 126529 115221 123138 133316 140836 136563 83990 50252 44211 40333 32602 41305

Angola 122338 108913 114809 118858 125159 137410 160597 185384 214530 228964 216805 227517

Antigua and Barbuda 5 8 15 -9 0 -3 23 6 2 18 -9 -9

Argentina -29568 -28633 -25364 -7985 -7980 5831 -1459 4184 5961 7658 -3409 -3622

Armenia -2490 -3082 -3682 -3993 -3897 -1663 -732 4053 6922 5500 2819 2110

Aruba (Not-ODA) -45 -85 -98 -82 -80 -51 -59 -50 -35 -11 -22 -6

Asia 16897596 16943595 18131467 11290966 9594390 9412866 8877802 8544960 8315063 8304442 5723899 5379233

Australia -40209 -38005 -36137 -25482 -24593 -21892 -28413 -31734 -35915 -40991 -56983 -63815

Australia/New Zealand -50603 -47560 -45000 -31898 -30048 -25223 -32021 -35284 -41107 -46612 -65823 -73849

Austria -20596 -17418 -18675 -10501 -8743 -3560 -4989 -9172 -10531 -12258 -18757 -21180

Azerbaijan 4687 4635 5060 4056 8676 12202 2907 15058 25058 19652 16243 13611

Bahamas -75 -73 -95 -76 -22 57 75 96 151 236 78 123

Bahrain 1341 1222 793 376 -98 36 -289 -439 -455 -482 -887 -1271

Bangladesh 455082 508150 524436 918822 603177 700455 657664 590621 516936 431960 311463 259754

Barbados 130 134 224 200 69 234 193 160 104 137 75 69

BelarusÂ -6622 -17037 -20844 -15588 -11037 -665 7084 6695 24268 47296 51202 45288

Belgium -31617 -29675 -27311 -16197 -13447 -7059 -10291 -13817 -16003 -16267 -22263 -24900

Belize 173 128 106 90 59 99 26 15 126 248 188 189

Benin 50381 47893 45498 44467 46121 44729 45795 49974 50474 58389 60240 60495

Bhutan 4641 5062 5360 5777 5799 5785 5457 5099 4050 3156 2391 1903 Bolivia (Plurinational State of) 38345 42863 46297 50965 54728 51629 45074 40301 37422 36198 32079 30734

Bosnia and Herzegovina 254 2119 12296 6276 -1086 -595 -180 -100 18727 762 -51 736

BotswanaÂ 3290 3572 3791 4227 4003 4018 3783 3682 5846 13087 21006 26113

Brazil 283757 313225 319211 326746 315617 345211 331982 295985 273772 273576 205483 187663

Brunei Darussalam -32 40 -89 -118 -140 -71 -181 -184 -228 -229 -398 -443

Bulgaria (Not-ODA) -12910 -18341 -24570 -18861 -13858 -2509 2091 7495 13462 21262 16602 15882

Burkina Faso 99028 98509 98908 100420 101959 103556 94348 103047 119816 134984 137982 139551

Burundi 34948 38248 41263 45105 49357 52237 57459 67020 86001 82447 84057 92240

Cambodia 70263 95246 129767 399535 86542 82689 60746 76280 72226 66179 47601 33313

Cameroon 65191 69765 73442 79556 81798 86982 92617 104234 119331 144815 170465 185122

Canada (Not-ODA) -70707 -70040 -70741 -57582 -52693 -41804 -50294 -55535 -57189 -57320 -77645 -82532

Cape Verde 1577 1209 1854 1891 1496 1689 2118 942 845 747 413 230

Caribbean 73404 66475 56781 49097 40856 49061 54077 54179 60147 62577 50114 44356

Central African Republic 27060 26263 27711 28218 26290 25417 27652 34883 43997 52494 56260 55256

Central America 265034 239807 221465 242916 224149 207615 169589 127930 91474 60946 -2923 -15044

CENTRAL ASIA 65366 65414 65229 68113 73241 97665 103290 112416 156851 190202 170488 181667

Chad 44945 49315 52915 57434 59737 66396 71498 80532 95803 112424 128989 141953

Channel Islands -494 -444 -406 -318 -280 -173 -203 -209 -215 -215 -329 -372

Chile 15040 22141 22659 18521 11881 6726 -2020 -6207 -8977 -9544 -20079 -19996

China 6094887 6635471 8532723 2164237 738775 807839 787360 877419 833243 976983 -216208 -244637

China, Hong Kong SAR -5308 -6428 -7968 -8425 -9299 -7987 -10404 -11505 -12174 -14222 -18994 -20913

China, Macao SAR -57 -91 -296 -389 -415 -332 -375 -498 -566 -585 -789 -944

Colombia 68316 54399 51282 53905 49576 51886 34067 36925 46841 43999 28163 26679

ComorosÂ 2122 2406 2354 2496 2477 2786 3022 2958 3095 3437 3595 3841

Congo 8617 8317 8024 8708 9586 11383 12464 15086 20864 27491 30898 30937



1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

Costa Rica 1356 1186 637 -207 -1228 -1702 -3428 -3917 -3986 -3847 -5396 -5531

Côte d'Ivoire 61853 66502 68868 74796 75981 81973 87908 100115 128963 188396 225267 215878

Croatia 2782 744 -1031 -604 -592 44 3091 1886 3610 2360 1926 628

Cuba -2311 -5040 -8273 -13816 -18274 -16319 -15686 -13360 -9357 -10006 -14577 -15693

Curaçao -122 -184 -172 -186 -178 -137 -157 -137 -96 -20 -54 -97

Cyprus -2070 -1907 -1975 -2111 -1769 -1381 -1433 -1492 -1518 -1475 -1894 -1909

Czech Republic -27228 -26793 -23582 -9637 -1009 6787 13098 13472 11445 7035 -583 -6131 Dem. People's Republic of Korea 29486 70952 142841 73516 53583 21553 20741 18122 13453 89292 59813 71140

Democratic Republic of the Congo 377883 263451 179171 201441 230940 284947 334294 439972 535827 692117 707871 782712

Denmark -23455 -20921 -19439 -15062 -14001 -9209 -7015 -5408 -3556 -2923 -6883 -8869

Djibouti 742 839 966 1294 1497 2040 2820 3201 4320 4633 4758 4742

Dominican Republic 25380 25054 22752 20392 16448 15815 14559 12777 11118 12103 9685 9971

Eastern Africa 1160088 1223695 1272600 1384699 1502486 1714223 1941672 2182008 2678781 2768567 2747019 2448987

Eastern Asia 6233880 6684470 8516846 2078436 529237 595616 512680 523685 452260 642036 -750887 -831180

Eastern Europe -156827 -423083 -541106 -314127 -161049 232399 450176 441114 897452 1217494 1331891 1142578

Ecuador 25889 25902 24260 26719 25956 25773 19903 14844 10471 6746 -1248 -1181

Egypt 336328 296609 288873 294524 299736 273449 237551 180018 154970 128282 113157 125748

El Salvador 21336 20466 19869 20516 21512 26224 28507 17669 9863 10172 7956 8390

Equatorial Guinea 4376 4213 4731 3438 4168 4477 3948 5056 5660 6396 7058 7302

Eritrea 36360 31995 28869 25450 23184 20704 27595 37855 35394 27950 25995 27410

Estonia -2900 -2143 -3092 -2113 -1458 969 1846 1967 4640 4658 3370 2051

Ethiopia 368835 373418 367168 396045 442167 513711 613518 634232 682957 688838 612011 482915

EUROPE -1270362 -1455017 -1591360 -1091537 -887130 -222726 -80472 -161715 311978 579014 384200 69239

Fiji 1405 1081 1129 1161 1149 1166 1430 1377 1445 1595 1534 1763

Finland -12137 -10097 -7922 -3602 -4070 -2886 -5332 -4834 -5340 -6638 -10646 -12120

France -153274 -150543 -159247 -119063 -122194 -90208 -100041 -113615 -118917 -121986 -160247 -171625

French Guiana 90 81 56 11 11 48 18 -15 -49 -74 -141 -144

French Polynesia 381 220 203 211 228 187 292 163 169 117 8 -83

Gabon 6926 6922 7217 6811 6142 5795 4991 4347 4901 6214 7576 7565

Gambia 8881 6526 7803 8925 8627 8598 8386 8724 9716 10397 10554 11222

Georgia 1163 -317 -2422 -803 1809 -2305 -2588 2066 4970 5863 3629 4455

Germany -246843 -216282 -210434 -130678 -98413 -38200 -61059 -90829 -95968 -116880 -188507 -216469

Ghana 58642 65014 71382 79345 85239 92454 98154 99824 97781 120694 130943 127974

Greece -23480 -19501 -21490 -18188 -18249 -13394 -16902 -18386 -23185 -22144 -29623 -28712

Grenada 85 87 124 90 84 139 134 115 161 167 123 111

Guadeloupe 668 412 363 122 -36 7 -75 -176 -277 -358 -621 -717

Guam 69 59 38 17 -19 -3 -29 -42 -47 -62 -156 -176

Guatemala 39780 43140 44297 44840 41394 43830 42667 38449 34172 29582 21250 20996

Guinea 79488 82425 83927 74788 71352 68208 81167 75495 80600 92933 98858 91524

Guinea-Bissau 8092 7800 7712 7953 10835 11345 12019 12446 12940 13656 14133 14875

Guyana 216 737 966 1474 2020 2717 2561 2570 2719 2875 2833 2580

Haiti 50872 51892 52613 49994 50245 54655 59454 57708 57011 56674 54772 50082

Honduras 20423 21380 20080 20986 21655 19157 15650 11587 10337 8988 6841 6954

Hungary -20593 -23551 -24320 -13144 -8070 9606 21083 24153 29698 27896 19316 15930

Iceland -941 -924 -916 -740 -663 -625 -586 -598 -604 -583 -802 -847

India 6530623 6267018 5838170 5461099 5000700 4764246 4675998 4773335 4871604 4760898 4195630 4012986

Indonesia 1095777 1036888 938583 866258 760214 720480 637749 561686 510520 479216 396518 374563

Iran (Islamic Republic of) 268334 257639 215817 231996 260989 267202 362530 240116 109146 89023 49811 42304

Iraq 103967 72228 57158 45955 38437 39921 62130 34453 15482 21481 24470 52317

Ireland -9562 -4446 -2605 -2292 -8770 -9068 -5859 -2407 -2724 -2522 -6329 -8574



1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

Israel -7035 -7530 -7789 -7033 -7242 -6082 -6704 -7188 -8030 -9426 -13419 -15306

Italy -169428 -155898 -155031 -123641 -128357 -88510 -97020 -115499 -120810 -132899 -185522 -204107

Jamaica 435 -224 -1984 -1671 -1914 -972 -742 235 1799 2880 2556 2301

Japan -136017 -166640 -200920 -213975 -266360 -274024 -319866 -366815 -376092 -390369 -493396 -519217

Jordan 4696 5317 5297 5279 4335 3319 2404 1488 1527 2085 429 848

Kazakhstan 18535 17196 18415 18471 19586 28161 28770 27922 49049 78890 67665 66566

Kenya 77611 85112 84709 90133 91451 99101 98652 118381 155073 241054 288612 263753

Kiribati 346 307 318 289 331 356 340 329 323 285 229 219

Kuwait 663 435 254 173 -350 -580 -1278 -2132 -1935 -1294 -2033 -1818

Kyrgyzstan 8174 7717 7424 7308 7528 9477 10808 10498 12158 12942 12895 15306 Lao People's Democratic Republic 24516 22008 26685 29766 32347 35455 37189 38011 34418 27861 20192 17923

LATIN AMERICA AND THE CARIBBEAN 829590 835897 816615 866378 802751 836421 729874 636617 579997 540579 318330 276301

Latvia -4430 -5900 -6514 -4265 -2266 2199 3598 3414 9133 8944 6731 6596

Least developed países 3492591 3626468 3773366 4027358 4541049 4917448 4827702 5031478 5590883 5726267 5462653 5202134

Lebanon -1053 -1353 -1396 -543 -429 1120 1488 1416 1164 59 -3307 -6329

Lesotho 8562 8406 8213 9583 10371 10812 10037 9699 8538 16249 26812 25380

Less developed regions 21784461 22037358 23403396 16920042 15433781 15673497 15331851 15410958 16063288 16806140 14476682 13975915

Less developed regions, excluding China 15693373 15418564 14893436 14777665 14723431 14889020 14572196 14563064 15258063 15856898 14740557 14271685

Less developed regions, excluding least developed countries

18291870 18410890 19630030 12892684 10892732 10756050 10504149 10379480 10472405 11079873 9014029 8773780

Liberia 25830 21940 19677 24178 25188 26381 27928 29520 28176 26434 30716 24339

Libya 22824 21322 17286 13382 9834 7935 5326 3884 2978 2392 -274 -1084

Lithuania -8827 -3895 -6486 -7309 -5584 -1206 968 1275 6962 8436 7091 9557

Luxembourg -694 -600 -713 -316 -178 28 -177 -356 -485 -533 -865 -995

Madagascar 73122 77926 81295 86285 90277 98517 99888 119676 122679 111108 93016 88734

Malawi 51806 58612 66327 76958 83427 98273 107884 119989 137241 152514 154206 138755

Malaysia 14675 10950 2651 4911 870 3942 2054 -25 647 2155 -5531 853

Maldives 1452 1899 1457 1769 1764 1566 1324 1181 863 564 111 -152

Mali 136554 147442 139324 133394 147679 139900 129428 122745 124762 138293 140542 146830

Malta -1022 -684 -609 -400 -393 -287 -397 -466 -486 -496 -691 -823

Martinique 535 281 68 27 -145 -162 -239 -369 -472 -558 -821 -908

Mauritania 10078 10623 10533 11011 11442 12206 11642 11992 13246 15306 16707 18808

Mauritius 970 1438 2523 660 679 1088 690 1043 1122 1388 1135 1141

Mayotte -4 69 143 119 90 37 28 -23 -50 -65 -141 -177

Melanesia 26377 29820 33361 34841 36077 30737 29251 30601 31916 34490 32909 32870

Mexico 134504 163841 117860 138386 125411 101908 68593 49502 31399 8006 -37367 -48648

Micronesia 630 576 509 561 546 634 657 669 673 574 295 229

Micronesia (Fed. States of) 94 97 102 119 102 104 139 169 200 230 201 190

Middle Africa 598192 482512 444791 524082 571594 671444 752091 869982 1041442 1271510 1326513 1438945

Mongolia 8818 9532 8992 8754 8471 10294 11630 11044 10141 9413 7512 7508

Montenegro -54 -163 -663 -681 -981 -760 -780 -664 -508 -274 467 508

More developed regions -2233552 -2392825 -2498285 -1777495 -1577463 -865829 -818051 -932768 -452506 -189436 -640867 -1024814

Morocco 94538 111020 117400 119585 117892 120548 98221 71732 55607 51527 44113 46901

Mozambique 150951 153512 156669 165782 170657 183653 204720 217363 226766 233508 254188 274802

Myanmar 325837 280010 270486 224252 223047 236897 235276 222990 212761 209358 185966 184563

Namibia 6028 5767 5567 5713 5774 5936 5563 5779 6749 10256 16121 10918

Nepal 151227 143376 155914 163697 168366 172582 164644 150415 130204 115016 91268 76171

Netherlands -62566 -56976 -55150 -43657 -39348 -30419 -31249 -30337 -28138 -26122 -34637 -41989

New Caledonia 326 251 199 191 136 134 84 42 21 -6 -104 -163



1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

New Zealand -10395 -9555 -8863 -5455 -6416 -3330 -3608 -3550 -5193 -5621 -8839 -10034

Nicaragua 17474 18011 17099 16944 16505 18177 18143 15411 10340 8369 5192 3964

Niger 90109 62099 50522 74882 103582 116878 124719 139633 147972 148727 141013 97573

Nigeria 795717 758635 815964 871388 921202 998258 1068332 1245744 1443761 1643421 1738161 1714396

Northern Africa 675703 671150 672779 684112 685855 668840 565321 460887 432285 412886 371867 392705

NORTHERN AMERICA -776569 -723608 -661005 -440086 -393925 -343856 -385692 -368954 -347285 -331469 -465848 -500987

Northern Europe -322983 -286758 -270406 -202487 -173676 -89736 -96477 -97075 -88916 -92151 -162590 -191713

Norway -21604 -19607 -17977 -14586 -13495 -10125 -9869 -8056 -8129 -8585 -11205 -12604

OCEANIA -12866 -11131 -10061 491 1322 5819 -4046 -3203 -7779 -10889 -32242 -40466

Oman 8458 7840 7275 7005 6667 6193 4863 3271 1799 828 -733 -1497

Other non-specified areas 1566 -10159 -14042 -13503 -18712 -15043 -16926 -17522 -15278 -12934 -27884 -29277

Pakistan 422366 482409 549917 413512 417293 452862 499976 548473 554773 562061 469874 486650

Panama 993 992 610 691 73 -79 -570 -786 -777 -571 -1587 -1359

Papua New Guinea 23133 26009 26889 30598 33246 31967 27849 26677 28028 30647 29766 29703

Paraguay -2931 -1584 -1027 456 744 3084 4347 5594 6368 7291 5186 5481

Peru 85978 93407 95180 99432 84272 81685 67428 57394 48293 37158 17228 13332

Philippines 42913 51881 61361 75654 81525 130863 138073 129093 144546 166644 163163 187441

Poland -25605 -60220 -71554 -52423 -42460 -6689 4682 26807 45201 40858 11211 9781

Polynesia 1151 1027 1002 1021 1005 1157 941 810 739 660 377 283

Portugal -7119 -4583 -6961 -2873 -6875 -3671 -8612 -10500 -8978 -9968 -17826 -20683

Puerto Rico -5458 -7014 -7052 -6232 -5911 -5478 -4820 -4400 -1945 -1396 -3600 -3771

Qatar 83 66 26 -38 -169 -232 -451 -766 -813 -816 -1359 -2281

Republic of Korea 127150 149269 87698 127862 47290 53316 40520 13441 -467 -15542 -60941 -94840

Republic of Moldova 2685 1815 1468 2244 3909 8713 12862 9991 13352 18142 17544 17597

Réunion 2123 1445 1031 663 253 273 121 -48 -179 -268 -652 -805

Romania -15222 -15114 -41871 -12606 -21126 971 12543 27212 41160 57129 41648 32295

Russian Federation 6013 -161945 -208760 -101741 -6469 194946 298759 257202 560402 757410 928373 764701

Rwanda 33214 38740 42594 46913 56610 67635 67327 95060 265568 92547 94302 58348

Saint Lucia 183 493 468 258 109 103 38 -2 68 148 108 32

Saint Vincent and the Grenadines 343 79 37 177 345 102 56 47 68 120 122 125

Samoa 663 687 667 681 689 668 577 483 383 320 201 188

Sao Tome and Principe 508 498 552 347 365 330 442 490 531 596 589 581

Saudi Arabia 41441 44485 47089 48796 41609 34114 24598 15605 7125 3951 -9074 -14701

Senegal 72859 47495 52132 59236 77285 70066 61835 53282 56728 68765 66245 57849

Serbia 1623 -1444 -5910 -5559 -5936 546 2129 4350 6744 15909 16610 15104

Seychelles 59 59 16 32 -9 -2 -5 21 48 68 63 94

Sierra Leone 49061 52034 55873 59935 56380 57507 61760 75433 91085 90052 87654 84662

Singapore -3889 -2908 -1242 -2339 -2032 -1401 -2592 -3429 -4825 -5431 -8472 -10807

Slovakia -9571 -13132 -14022 -8455 -4882 -421 1972 4247 5624 6097 3938 3597

Slovenia -3138 -3156 -3053 -1236 -1071 -157 573 -52 58 -346 -1838 -3305

Solomon Islands 761 829 856 952 1050 1101 1248 2022 1966 1830 1427 1326

Somalia 46182 48805 51105 54342 56931 74406 85693 87339 100116 86833 88737 92719

South Africa 137011 133685 138883 149472 165320 148731 152612 136872 141867 242029 440670 517130

South America 491152 529615 538369 574366 537746 579745 506209 454508 428375 417056 271139 246989

South Sudan 80722 70052 70543 72934 76912 86595 97133 98847 84657 79257 82910 90261

South-Central Asia 8258416 7983287 7508486 7214164 7059770 6660332 6679998 6596863 6527815 6374875 5482552 5245284

South-Eastern Asia 1453780 1407970 1378784 1299943 1338829 1532077 1186129 1066674 951757 910602 948814 935855

Southern Africa 129962 132277 146706 174715 159950 157553 164080 155774 157573 276683 524727 600177

Southern Asia 8193050 7917873 7443257 7146051 6986529 6562667 6576708 6484446 6370964 6184674 5312063 5063616

Southern Europe -253648 -253811 -287965 -236840 -251541 -180558 -210352 -230235 -209739 -234384 -336807 -378868



1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

Spain -69329 -78632 -95376 -84566 -86744 -75435 -91728 -89266 -86051 -88420 -117562 -136304

Sri Lanka 1830 8470 4669 11175 21008 13599 9023 12130 16045 34872 4718 9112

State of Palestine 5715 4989 3542 7328 7680 8680 8439 2602 2583 2294 1292 1504

Sub-Saharan Africa 3207816 3383647 3546676 3832221 4053053 4450304 4811013 5369598 6326523 7122141 7535610 7375076

Sudan 57780 62052 67395 76022 83604 103408 123447 143188 168640 187804 183854 180338

Suriname 477 530 388 302 384 297 497 548 707 990 1033 958

Swaziland 3539 3868 4271 4788 4799 4926 4650 4685 5027 8471 13053 13141

Sweden -40290 -36782 -36465 -30938 -29858 -21520 -22563 -22703 -22522 -22675 -26915 -28699

Switzerland -21224 -19733 -20215 -17549 -18421 -15306 -15886 -17262 -16645 -17758 -22816 -25388

Syrian Arab Republic 26919 26607 25882 24899 19345 16281 10101 5614 2176 1285 -6706 -8622

Tajikistan 8796 9881 9487 11201 12571 14858 18032 21576 26975 26643 20774 20500

TFYR Macedonia -45 -777 595 1848 3156 -117 658 138 1044 1365 881 1359

Thailand 98190 109250 102471 115430 98894 101862 77008 22520 38746 71894 59562 42402

Timor-Leste 10699 10569 10164 14399 10907 10563 10576 9115 8853 6489 4348 3698

Togo 25283 24712 26492 23389 24440 25959 26213 27982 31416 40785 44996 49684

Tonga -43 -4 -10 -7 -8 55 56 57 88 111 92 101

Trinidad and Tobago 292 53 -346 31 503 1075 1369 1647 1983 2530 2537 2999

Tunisia 50201 55029 54584 46267 32902 25608 15305 10558 4683 1419 -2629 -1591

Turkey 313790 326664 303332 280027 261229 244899 203895 162303 132531 96898 36296 16030

Turkmenistan 8134 7733 8373 8260 8883 10671 11487 12909 15114 15173 15352 16780

Uganda 75931 80498 84114 90293 104011 129838 157000 198574 259091 300769 277668 255639

Ukraine -47775 -88765 -113050 -83916 -56048 21659 76002 63840 152840 234370 242640 243637

United Arab Emirates 816 676 599 489 359 98 -655 -1459 -2039 -2633 -5122 -7897

United Kingdom -203577 -173219 -159686 -117346 -97198 -44588 -54434 -59257 -66321 -69804 -116362 -137520

United Republic of Tanzania 100242 114775 132570 152079 165711 182487 207963 243275 311377 363057 355811 307942

United States of America -705832 -653541 -590186 -382444 -341192 -302107 -335473 -313507 -290214 -274290 -388273 -418525

United States Virgin Islands 0 -23 -45 -34 -91 -89 -72 -90 -97 -115 -165 -165

Uruguay -7108 -5965 -6213 -3795 -2265 -394 -674 -4 234 117 -1510 -1859

Uzbekistan 21728 22887 21530 22874 24672 34497 34193 39512 53556 56554 53802 62515

Vanuatu 614 632 628 597 579 577 507 483 456 424 285 241 Venezuela (Bolivarian Republic of) 12757 12745 10771 7532 2713 5023 4488 2390 4618 10072 5528 6372

Viet Nam 89381 72292 50151 61699 205357 68964 57346 49458 38983 24606 -2446 8457

Western Africa 1589269 1444507 1522142 1702886 1774816 1873626 1943744 2156892 2438274 2791973 2944415 2894462

Western Asia 646275 612153 562215 519298 465371 445993 405753 318898 278341 238789 131732 123167

Western Europe -536904 -491366 -491882 -338082 -300864 -184831 -223819 -275520 -286819 -311945 -448294 -502757

Western Sahara 314 478 711 1016 1052 1329 1482 1256 1196 1128 1045 1088

WORLD 22378571 22359125 23681287 17540402 15953937 16333040 16360286 16561451 17603116 18591914 17428328 16821851

Yemen 143219 127728 118033 111220 100893 97223 90264 84445 91792 95020 91088 93921

Zambia 28613 32069 35782 41189 43728 51684 65855 95491 132401 161862 155454 128531

Zimbabwe 19675 22491 25450 29837 33343 37876 35574 41755 71103 137629 171295 138102


ANNEX 3: BURDEN OF GLOBAL HEALTH INEQUITY IN PROPORTION OF DEATHS WHICH ARE AVOIDABLE BY GHE

Country 1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

AFGHANISTAN 74.30% 75.80% 76.30% 77.70% 79.60% 79.90% 77.50% 79.50% 77.70% 77.00% 73.70% 71.50%

AFRICA 61.50% 63.20% 64.00% 66.10% 66.60% 69.10% 69.30% 70.50% 73.10% 75.00% 73.40% 72.30%

Albania 20.00% 16.30% -2.50% -6.50% -6.70% -2.80% -7.10% -10.30% 2.30% 0.20% -25.30% -21.40%

Algeria 56.10% 54.20% 55.50% 58.10% 58.80% 59.20% 46.40% 33.80% 31.00% 29.10% 22.30% 24.90%

Angola 76.20% 71.20% 73.20% 74.50% 76.80% 79.30% 81.00% 82.70% 84.50% 85.20% 82.40% 82.20%

Antigua and Barbuda 1.10% 1.80% 4.10% -2.20% 0.10% -0.70% 6.40% 1.70% 0.70% 5.40% -2.60% -2.80%

Argentina -19.80% -18.70% -15.40% -4.50% -4.30% 3.00% -0.80% 2.10% 3.00% 3.90% -1.70% -1.80%

Armenia -17.10% -20.70% -25.20% -31.40% -30.70% -11.40% -4.50% 18.90% 29.40% 25.70% 14.20% 11.50%

Aruba (Not-ODA) -13.20% -26.90% -31.50% -27.40% -27.00% -17.10% -19.70% -16.40% -10.40% -2.70% -4.80% -1.10%

Asia 52.80% 53.80% 55.90% 44.80% 40.10% 41.00% 38.80% 37.00% 36.10% 36.00% 25.60% 23.80%

Australia -63.20% -57.00% -50.20% -32.00% -29.70% -27.50% -36.60% -39.90% -46.30% -55.00% -81.00% -95.10%

Australia/New Zealand -64.40% -57.90% -51.10% -33.00% -29.70% -25.70% -33.30% -35.90% -43.40% -51.00% -76.60% -89.90%

Austria -29.70% -25.20% -27.40% -14.90% -12.60% -5.50% -8.40% -18.00% -23.20% -28.50% -46.70% -58.20%

Azerbaijan 11.80% 10.80% 11.30% 10.20% 22.10% 28.20% 8.00% 32.20% 44.60% 39.30% 32.30% 27.90%

Bahamas -9.60% -9.10% -11.00% -8.20% -2.20% 5.50% 6.80% 8.30% 12.20% 17.60% 5.60% 7.80%

Bahrain 52.40% 49.40% 39.00% 23.80% -7.30% 2.60% -20.50% -31.80% -32.80% -33.00% -54.20% -62.30%

Bangladesh 52.10% 54.90% 54.90% 67.70% 58.30% 62.80% 60.40% 57.40% 53.90% 49.30% 38.30% 34.10%

Barbados 5.70% 6.40% 8.90% 7.80% 3.50% 11.70% 10.20% 9.20% 6.40% 8.40% 4.60% 4.30%

BelarusÂ -8.10% -25.30% -34.70% -27.10% -18.00% -1.00% 9.30% 9.10% 27.60% 43.30% 44.30% 43.00%

Belgium -37.40% -36.30% -32.80% -18.90% -15.80% -8.80% -14.00% -21.00% -26.80% -27.50% -39.00% -47.90%

Belize 17.70% 13.10% 11.10% 9.80% 6.60% 11.80% 3.30% 1.90% 14.40% 24.70% 17.80% 17.40%

Benin 72.20% 71.40% 68.80% 65.50% 63.10% 66.90% 68.80% 71.90% 71.50% 74.00% 71.70% 70.70%

Bhutan 72.10% 74.70% 75.40% 76.90% 75.70% 75.40% 73.00% 70.80% 67.10% 63.10% 52.40% 44.80%

Bolivia (Plurinational State of) 56.70% 60.30% 62.30% 65.30% 66.40% 66.40% 62.80% 59.50% 57.10% 55.90% 49.70% 48.00%

Bosnia and Herzegovina 1.00% 7.60% 30.20% 18.80% -4.70% -2.70% -0.80% -0.40% 47.50% 3.70% -0.20% 2.80%

BotswanaÂ 41.40% 43.50% 45.80% 49.70% 47.70% 48.10% 45.10% 43.70% 54.40% 72.50% 79.10% 82.60%

Brazil 32.80% 34.60% 34.10% 35.00% 33.80% 36.70% 34.80% 31.90% 30.10% 29.60% 21.60% 19.50%

Brunei Darussalam -4.70% 6.10% -12.80% -17.00% -18.50% -8.90% -25.10% -24.70% -31.50% -30.80% -51.50% -51.10%

Bulgaria (Not-ODA) -19.20% -31.70% -46.70% -33.70% -21.90% -3.60% 2.80% 9.70% 17.20% 25.70% 21.00% 21.50%

Burkina Faso 72.00% 70.70% 72.50% 73.70% 74.00% 75.50% 73.30% 75.00% 77.80% 80.10% 78.60% 78.30%

Burundi 58.70% 61.80% 64.30% 67.30% 69.50% 72.20% 73.40% 75.80% 80.40% 80.80% 79.40% 79.80%

Cambodia 60.20% 69.00% 75.90% 92.40% 73.00% 64.50% 56.30% 68.30% 66.20% 64.00% 52.90% 42.40%

Cameroon 58.10% 60.80% 61.80% 63.90% 63.70% 65.80% 66.20% 68.20% 70.70% 74.60% 75.30% 76.40%

Canada (Not-ODA) -69.10% -65.60% -64.50% -51.70% -45.30% -34.60% -41.60% -44.80% -45.40% -44.80% -62.10% -65.30%

Cape Verde 48.10% 42.30% 49.60% 48.60% 40.90% 44.30% 53.20% 37.20% 35.60% 33.40% 19.80% 12.10%

Caribbean 28.80% 27.30% 23.80% 21.80% 18.90% 23.00% 24.30% 24.10% 26.30% 27.30% 21.60% 19.40%

Central African Republic 66.60% 64.30% 67.70% 69.10% 67.50% 68.10% 69.10% 73.70% 78.10% 81.20% 81.00% 81.00%

Central America 38.40% 35.50% 33.00% 35.10% 32.40% 31.70% 27.30% 21.90% 16.40% 11.30% -0.50% -2.80%

CENTRAL ASIA 24.10% 22.50% 21.30% 22.80% 24.20% 31.20% 32.20% 34.20% 42.90% 49.10% 44.70% 46.40%

Chad 61.80% 64.90% 67.10% 70.10% 70.70% 74.10% 75.50% 77.40% 79.90% 82.20% 81.90% 82.60%

Channel Islands -53.50% -48.50% -42.50% -32.90% -28.40% -17.80% -21.90% -23.40% -26.10% -27.60% -45.10% -53.10%

Chile 18.40% 24.80% 24.60% 21.90% 15.50% 10.20% -3.40% -10.90% -16.10% -16.80% -36.50% -33.70%

China 49.80% 53.40% 60.90% 28.70% 11.70% 13.50% 12.90% 13.50% 12.80% 14.80% -3.60% -3.90%

China, Hong Kong SAR -33.30% -38.00% -46.00% -48.50% -54.60% -42.00% -51.50% -52.00% -53.80% -62.10% -81.50% -93.50%

China, Macao SAR -3.20% -5.80% -21.10% -29.10% -31.00% -27.00% -29.80% -39.70% -45.10% -44.90% -58.70% -67.90%

Colombia 33.80% 29.00% 27.40% 28.70% 26.90% 29.10% 20.70% 21.60% 25.60% 24.10% 15.10% 13.80%

ComorosÂ 56.10% 60.30% 59.10% 62.50% 62.20% 65.30% 65.60% 65.00% 66.30% 68.60% 66.40% 67.10%

Congo 49.90% 49.30% 47.70% 49.70% 50.90% 55.90% 57.10% 61.30% 68.20% 73.60% 73.30% 72.60%


Country 1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

Costa Rica 10.30% 8.80% 4.70% -1.70% -11.10% -17.00% -37.40% -41.50% -40.50% -37.10% -48.10% -44.70%

Côte d'Ivoire 69.80% 71.10% 70.00% 70.20% 67.90% 68.40% 67.40% 68.50% 72.50% 78.80% 79.90% 79.30%

Croatia 7.80% 2.10% -2.80% -1.40% -1.50% 0.10% 8.50% 5.40% 10.00% 6.90% 5.40% 1.90%

Cuba -3.80% -8.90% -14.50% -26.90% -38.70% -36.70% -34.90% -28.20% -18.70% -20.80% -30.00% -32.10%

Curaçao -11.40% -19.20% -21.90% -21.70% -23.50% -20.00% -24.30% -21.00% -14.30% -2.80% -7.50% -13.20%

Cyprus -56.70% -53.10% -59.70% -69.90% -50.30% -40.80% -43.70% -46.10% -46.30% -42.70% -51.30% -47.50%

Czech Republic -33.00% -34.80% -29.80% -10.90% -1.00% 7.00% 13.40% 14.90% 14.10% 9.40% -0.80% -9.60% Dem. People's Republic of Korea 24.80% 44.70% 61.10% 46.20% 38.50% 21.50% 20.70% 17.60% 12.90% 48.50% 35.90% 38.90%

Democratic Republic of the Congo 74.70% 68.40% 58.00% 62.20% 65.30% 69.20% 73.20% 76.90% 79.30% 83.00% 81.50% 82.40%

Denmark -77.70% -68.40% -59.40% -43.60% -39.70% -25.80% -19.10% -14.80% -10.00% -8.60% -22.10% -30.70%

Djibouti 54.50% 58.00% 57.00% 59.20% 57.00% 59.20% 62.80% 62.00% 66.40% 68.30% 66.40% 65.60%

Dominican Republic 49.10% 48.00% 44.90% 42.70% 37.10% 37.30% 34.30% 30.40% 26.80% 28.00% 21.40% 21.30%

Eastern Africa 61.80% 63.80% 64.60% 66.80% 67.80% 71.10% 72.80% 74.60% 78.00% 78.40% 75.70% 72.80%

Eastern Asia 45.80% 49.00% 56.10% 24.10% 7.20% 8.60% 7.30% 7.10% 6.00% 8.40% -10.60% -11.30%

Eastern Europe -7.00% -22.00% -30.70% -16.70% -7.70% 9.80% 17.30% 17.40% 31.40% 39.30% 40.40% 38.00%

Ecuador 38.50% 38.60% 36.90% 39.80% 38.70% 39.80% 33.10% 26.30% 19.60% 13.20% -2.50% -2.20%

Egypt 58.80% 55.50% 53.90% 54.70% 54.80% 53.80% 49.30% 41.90% 38.20% 34.00% 29.00% 30.70%

El Salvador 46.80% 46.00% 44.80% 46.10% 47.00% 53.60% 55.80% 44.40% 30.80% 31.80% 25.70% 27.20%

Equatorial Guinea 64.80% 62.20% 69.40% 71.40% 69.50% 66.50% 73.40% 75.50% 76.90% 79.00% 78.50% 78.20%

Eritrea 77.90% 76.50% 73.90% 70.90% 68.20% 63.50% 73.40% 78.10% 77.40% 73.80% 68.10% 66.80%

Estonia -28.60% -18.60% -31.00% -20.20% -12.90% 7.60% 14.00% 15.30% 31.90% 33.70% 26.20% 18.40%

Ethiopia 66.00% 67.80% 67.50% 69.40% 70.90% 74.60% 77.30% 77.30% 77.60% 77.40% 72.60% 66.80%

EUROPE -24.30% -30.20% -34.20% -22.50% -17.50% -4.20% -1.50% -3.20% 5.90% 10.60% 6.90% 1.40%

Fiji 32.60% 25.70% 26.60% 27.30% 28.00% 28.40% 33.90% 32.50% 34.50% 37.40% 33.70% 36.00%

Finland -35.40% -30.80% -23.20% -10.00% -11.40% -8.40% -16.50% -15.00% -17.50% -23.10% -39.20% -46.80%

France -36.50% -38.70% -42.10% -30.60% -32.10% -24.70% -29.30% -36.80% -42.20% -43.40% -58.40% -68.20%

French Guiana 22.60% 20.50% 14.20% 3.30% 3.10% 12.30% 4.60% -3.40% -10.70% -15.50% -26.00% -23.50%

French Polynesia 38.70% 25.80% 23.30% 23.70% 24.60% 20.50% 30.00% 17.30% 17.50% 12.50% 0.80% -8.40%

Gabon 55.70% 57.80% 59.50% 59.20% 56.40% 56.50% 52.20% 48.20% 50.90% 56.80% 59.20% 58.80%

Gambia 75.50% 70.40% 73.80% 75.90% 74.30% 74.60% 72.80% 72.30% 73.40% 74.30% 71.80% 72.20%

Georgia 2.50% -0.70% -6.00% -2.50% 5.10% -6.90% -7.40% 5.70% 13.50% 16.70% 10.80% 13.60%

Germany -38.40% -33.10% -31.40% -18.50% -13.80% -5.60% -9.80% -16.40% -18.90% -24.00% -40.10% -49.50%

Ghana 53.20% 56.10% 57.10% 59.90% 61.20% 64.70% 65.40% 64.80% 63.50% 67.80% 66.40% 64.70%

Greece -39.60% -31.40% -35.40% -29.30% -28.10% -20.20% -26.30% -30.00% -42.40% -39.00% -51.90% -50.40%

Grenada 12.40% 8.80% 12.80% 10.00% 11.10% 20.70% 20.50% 18.10% 24.60% 27.40% 21.80% 21.20%

Guadeloupe 21.70% 14.50% 13.00% 5.30% -1.70% 0.30% -4.10% -9.50% -15.20% -19.90% -34.60% -40.30%

Guam 12.50% 11.30% 7.60% 3.70% -4.30% -0.80% -6.20% -8.30% -8.70% -11.10% -29.10% -33.30%

Guatemala 53.90% 56.50% 57.00% 57.70% 54.90% 57.20% 55.60% 52.60% 49.00% 44.90% 33.60% 32.50%

Guinea 77.50% 78.10% 76.20% 71.70% 69.30% 66.40% 74.90% 75.80% 74.80% 76.50% 76.10% 74.60%

Guinea-Bissau 62.40% 57.10% 58.50% 64.70% 70.30% 72.30% 74.30% 75.10% 76.10% 77.50% 76.20% 76.90%

Guyana 4.50% 13.70% 17.10% 24.30% 31.30% 41.50% 41.90% 44.20% 47.70% 51.50% 51.50% 52.60%

Haiti 61.50% 61.90% 61.30% 61.90% 62.10% 65.30% 66.40% 65.80% 66.00% 66.50% 63.60% 61.40%

Honduras 53.70% 55.70% 54.20% 54.70% 55.80% 53.00% 46.70% 38.50% 35.10% 31.60% 23.50% 23.20%

Hungary -22.70% -28.00% -30.10% -15.10% -8.60% 9.30% 19.20% 23.00% 28.70% 28.20% 20.90% 18.80%

Iceland -116.30% -111.20% - 102 00%

-74.90% -63.60% -65.80% -58.60% -60.20% -61.20% -57.80% -85.70% -91.40%

India 63.00% 62.80% 61.40% 60.70% 58.20% 58.10% 56.70% 56.60% 56.90% 56.30% 50.00% 48.20%

Indonesia 59.20% 58.20% 55.50% 54.20% 50.70% 50.70% 47.00% 43.60% 41.10% 39.30% 32.20% 30.50%

Iran (Islamic Republic of) 57.50% 58.40% 54.10% 55.50% 57.40% 57.00% 64.10% 52.50% 33.50% 29.40% 16.70% 13.80%

Iraq 63.00% 54.00% 47.50% 40.60% 34.60% 36.40% 46.60% 32.80% 17.90% 23.00% 22.70% 37.80%

Ireland -33.10% -18.80% -11.60% -9.80% -37.00% -35.70% -25.10% -11.20% -13.80% -13.20% -37.00% -55.30%


Country 1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

Israel -83.70% -75.90% -65.30% -49.90% -43.70% -32.90% -35.30% -38.50% -41.50% -46.20% -66.00% -75.40%

Italy -45.00% -42.10% -41.00% -32.00% -33.60% -23.20% -26.40% -34.00% -38.30% -44.00% -65.70% -79.10%

Jamaica 2.90% -1.60% -15.20% -13.20% -16.40% -8.80% -6.50% 1.90% 13.70% 20.90% 17.90% 16.70%

Japan -18.70% -27.00% -34.90% -38.60% -48.50% -52.90% -63.10% -73.80% -73.20% -72.50% -90.80% -95.60%

Jordan 43.70% 42.40% 38.40% 35.00% 27.80% 23.30% 16.90% 10.60% 10.20% 12.70% 2.60% 4.50%

Kazakhstan 19.00% 16.70% 16.60% 17.60% 18.80% 26.30% 26.70% 26.40% 40.20% 54.80% 50.50% 50.40%

Kenya 52.10% 54.00% 53.20% 54.80% 53.90% 56.30% 54.80% 58.50% 64.50% 73.70% 74.20% 71.40%

Kiribati 56.50% 52.70% 52.10% 49.00% 53.00% 55.50% 57.40% 54.30% 53.80% 51.90% 44.30% 42.50%

Kuwait 29.00% 19.30% 9.00% 4.50% -7.60% -12.00% -26.60% -46.80% -46.30% -27.40% -34.70% -25.70%

Kyrgyzstan 27.40% 25.70% 24.50% 25.30% 26.20% 32.60% 35.40% 34.80% 39.40% 41.90% 40.20% 46.20%

Lao People's Democratic Republic 59.40% 56.30% 61.30% 64.10% 65.10% 68.40% 69.20% 69.20% 66.60% 62.20% 52.30% 49.10%

LATIN AMERICA AND THE CARIBBEAN 31.20% 31.10% 29.90% 31.50% 29.40% 31.20% 27.80% 24.80% 22.90% 21.40% 12.40% 10.70%

Latvia -22.20% -34.40% -39.70% -24.00% -11.30% 9.70% 15.50% 15.50% 34.90% 36.10% 29.30% 30.30%

Least developed países 62.80% 64.60% 65.50% 67.30% 69.40% 72.10% 71.10% 71.70% 73.30% 73.60% 70.00% 68.30%

Lebanon -6.60% -8.40% -8.70% -3.40% -2.80% 7.20% 9.60% 9.50% 7.90% 0.40% -24.00% -48.40%

Lesotho 51.60% 52.80% 53.00% 57.90% 59.70% 62.10% 59.90% 59.40% 56.80% 72.30% 80.50% 80.70%

Less developed regions 54.00% 55.10% 56.80% 49.30% 46.40% 48.10% 46.70% 46.00% 46.70% 47.80% 41.10% 39.40%

Less developed regions, excluding China 56.10% 56.00% 54.90% 55.40% 54.80% 56.20% 54.80% 54.20% 55.00% 55.80% 50.80% 49.20%

Less developed regions, excluding least developed countries

52.60% 53.50% 55.40% 45.50% 40.70% 41.80% 40.40% 39.20% 39.20% 40.50% 32.90% 31.50%

Liberia 74.10% 71.20% 67.90% 73.20% 74.40% 74.90% 74.80% 76.90% 78.60% 76.40% 75.10% 69.40%

Libya 64.60% 62.40% 57.00% 50.10% 40.80% 35.50% 25.40% 19.20% 15.20% 12.30% -1.40% -5.70%

Lithuania -46.80% -14.60% -30.00% -36.90% -25.40% -4.90% 3.70% 5.00% 23.40% 27.60% 24.00% 32.10%

Luxembourg -23.00% -19.70% -23.90% -10.00% -5.50% 0.90% -6.20% -13.90% -20.90% -23.50% -39.30% -47.00%

Madagascar 62.30% 63.70% 64.40% 66.20% 66.50% 69.30% 69.40% 72.90% 72.90% 70.40% 63.20% 61.30%

Malawi 62.90% 67.60% 70.60% 74.10% 74.60% 77.50% 78.40% 78.70% 79.70% 81.50% 79.70% 77.30%

Malaysia 17.00% 13.50% 3.80% 6.50% 1.30% 5.80% 2.90% 0.00% 0.90% 2.70% -6.30% 0.90%

Maldives 66.40% 71.80% 67.80% 71.80% 70.40% 67.40% 62.00% 57.40% 49.30% 39.00% 9.90% -16.50%

Mali 79.90% 80.00% 77.10% 74.40% 79.00% 79.20% 78.80% 78.10% 78.70% 80.80% 79.40% 79.70%

Malta -39.70% -27.50% -27.30% -19.60% -20.10% -14.90% -20.90% -25.80% -27.80% -27.80% -36.30% -41.10%

Martinique 17.40% 10.30% 2.70% 1.10% -6.90% -9.00% -13.60% -21.60% -28.60% -34.80% -52.30% -58.20%

Mauritania 60.60% 61.60% 60.40% 61.30% 61.10% 63.20% 61.00% 61.00% 62.60% 65.40% 63.80% 65.20%

Mauritius 17.80% 21.60% 32.20% 11.30% 12.50% 20.30% 13.30% 18.60% 19.40% 22.20% 17.40% 17.10%

Mayotte -1.20% 20.10% 42.30% 34.40% 25.80% 11.40% 9.10% -7.30% -15.50% -18.60% -37.90% -49.90%

Melanesia 57.20% 59.70% 62.40% 62.50% 61.30% 61.50% 60.20% 60.60% 61.30% 62.60% 57.80% 56.60%

Mexico 29.30% 34.20% 25.90% 29.10% 26.10% 23.20% 16.90% 12.70% 8.20% 2.20% -10.10% -13.10%

Micronesia 30.50% 27.90% 25.20% 27.50% 27.20% 31.30% 30.70% 29.40% 28.40% 25.40% 13.80% 11.10%

Micronesia (Fed. States of) 20.30% 20.20% 20.80% 23.60% 20.40% 22.20% 27.50% 31.10% 34.60% 39.30% 36.20% 36.60%

Middle Africa 69.30% 64.40% 61.30% 66.50% 68.90% 72.50% 74.00% 76.20% 78.70% 81.70% 80.40% 81.10%

Mongolia 51.00% 51.90% 48.80% 48.00% 46.70% 53.30% 56.20% 55.00% 54.30% 55.20% 48.20% 47.40%

Montenegro -1.20% -3.80% -17.90% -20.40% -32.90% -26.20% -27.60% -23.30% -17.80% -8.50% 10.60% 11.60%

More developed regions -30.10% -34.40% -36.50% -25.10% -21.60% -11.70% -10.90% -12.90% -6.10% -2.50% -8.30% -14.20%

Morocco 49.20% 51.70% 52.50% 54.00% 53.90% 56.60% 51.20% 43.30% 36.70% 34.50% 28.70% 29.70%

Mozambique 69.50% 71.00% 71.70% 73.50% 73.80% 76.10% 77.70% 79.80% 80.80% 80.90% 79.90% 80.60%

Myanmar 64.00% 60.50% 59.80% 55.80% 55.10% 57.90% 57.20% 56.00% 55.20% 55.10% 49.90% 49.70%

Namibia 53.10% 52.90% 52.10% 53.20% 52.30% 54.00% 52.40% 52.50% 54.90% 64.50% 72.10% 64.10%

Nepal 66.90% 65.30% 67.40% 69.20% 69.40% 71.00% 69.30% 67.10% 63.40% 60.10% 50.80% 45.60%

Netherlands - 104 20%

-91.70% -82.80% -60.00% -51.70% -40.40% -41.90% -40.40% -37.30% -34.30% -46.50% -61.60%

New Caledonia 33.10% 28.30% 22.80% 21.80% 15.30% 15.30% 9.90% 5.10% 2.60% -0.70% -11.40% -16.40%


Country 1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

New Zealand -69.60% -61.70% -55.20% -30.00% -37.70% -17.90% -19.50% -19.00% -30.30% -33.50% -56.80% -66.70%

Nicaragua 55.50% 55.20% 53.90% 54.40% 52.50% 55.40% 54.30% 50.00% 39.90% 34.90% 22.90% 18.10%

Niger 78.10% 71.70% 66.70% 75.00% 79.70% 81.90% 82.10% 83.10% 83.10% 82.20% 78.30% 54.60%

Nigeria 68.50% 66.10% 69.30% 71.30% 71.90% 74.20% 74.70% 77.20% 79.50% 81.50% 80.30% 79.60%

Northern Africa 55.70% 55.10% 54.50% 55.40% 55.10% 55.70% 50.50% 44.80% 42.80% 41.50% 36.10% 36.40%

NORTHERN AMERICA -55.80% -50.20% -43.50% -27.70% -24.80% -22.80% -25.40% -23.70% -22.40% -21.60% -30.60% -33.20%

Northern Europe -47.10% -43.40% -40.10% -29.40% -24.70% -12.80% -14.30% -15.10% -14.70% -16.30% -31.60% -40.80%

Norway - 106 90%

-92.90% -76.20% -57.30% -50.80% -38.40% -38.00% -30.10% -32.70% -38.40% -56.40% -71.10%

OCEANIA -9.00% -7.80% -6.80% 0.30% 0.80% 3.80% -2.70% -2.10% -5.10% -7.20% -21.80% -27.90%

Oman 64.00% 63.10% 60.90% 60.20% 57.50% 54.50% 44.80% 33.40% 20.90% 11.50% -11.50% -24.60%

Other non-specified areas 2.00% -14.30% -20.00% -19.40% -28.20% -21.60% -22.00% -21.10% -17.00% -13.50% -29.30% -30.50%

Pakistan 51.20% 55.10% 57.20% 51.20% 50.60% 53.00% 53.40% 54.20% 53.80% 53.90% 46.50% 46.80%

Panama 9.60% 9.50% 5.80% 6.60% 0.70% -0.80% -6.00% -8.10% -7.70% -5.30% -13.60% -10.60%

Papua New Guinea 61.70% 64.80% 64.60% 67.50% 65.40% 67.00% 66.40% 64.70% 65.30% 66.70% 62.50% 61.40%

Paraguay -22.70% -11.20% -6.90% 2.80% 4.50% 16.70% 20.90% 24.40% 26.30% 28.60% 19.80% 20.00%

Peru 50.60% 52.80% 52.50% 53.90% 49.20% 49.40% 43.80% 39.30% 34.80% 28.80% 14.20% 11.00%

Philippines 16.70% 19.20% 21.70% 25.80% 26.70% 37.80% 38.10% 35.90% 38.20% 41.40% 37.50% 39.80%

Poland -10.20% -28.40% -36.30% -25.60% -18.80% -2.70% 1.80% 9.80% 16.30% 15.20% 4.40% 3.90%

Polynesia 31.50% 29.00% 28.00% 28.90% 29.20% 34.40% 29.80% 26.50% 24.80% 23.00% 13.30% 10.10%

Portugal -8.20% -5.50% -8.80% -3.80% -9.70% -5.20% -12.80% -16.40% -14.10% -15.90% -29.80% -37.10%

Puerto Rico -31.30% -50.40% -50.90% -46.30% -44.30% -40.80% -31.00% -26.80% -10.20% -7.10% -19.50% -21.70%

Qatar 20.90% 15.50% 5.50% -7.40% -29.80% -36.20% -59.80% -90.50% -91.70% -84.40% - 116 40%

- 124.50%

Republic of Korea 39.80% 43.60% 31.50% 38.90% 20.10% 23.50% 18.40% 6.70% -0.20% -8.10% -34.20% -57.70%

Republic of Moldova 9.00% 6.10% 5.00% 8.00% 13.00% 25.80% 33.40% 28.10% 35.70% 44.80% 44.00% 46.20%

Réunion 42.60% 33.70% 26.70% 19.10% 8.40% 10.10% 4.50% -1.80% -6.70% -9.90% -23.60% -28.40%

Romania -9.40% -9.50% -32.20% -8.00% -13.50% 0.60% 7.00% 14.50% 21.50% 28.20% 21.30% 18.10%

Russian Federation 0.60% -18.10% -25.30% -11.70% -0.70% 16.90% 23.50% 21.10% 38.10% 46.60% 50.50% 46.90%

Rwanda 57.30% 60.90% 63.60% 67.20% 70.60% 74.60% 73.40% 79.10% 92.90% 81.80% 78.00% 68.90%

Saint Lucia 19.80% 38.20% 37.80% 24.90% 13.20% 14.10% 5.70% -0.30% 8.60% 18.00% 13.70% 4.30%

Saint Vincent and the Grenadines 30.70% 11.00% 5.60% 20.40% 32.20% 15.50% 9.60% 8.70% 12.40% 21.00% 20.60% 21.60%

Western Samoa 48.10% 44.00% 43.60% 44.10% 44.60% 44.80% 45.90% 42.30% 37.30% 34.00% 23.10% 22.40%

Sao Tome and Principe 48.10% 49.60% 47.50% 40.90% 40.60% 39.20% 45.80% 49.70% 52.50% 56.70% 54.80% 54.10%

Saudi Arabia 54.50% 56.20% 56.50% 56.70% 50.30% 43.40% 32.00% 21.10% 10.60% 6.10% -13.70% -21.80%

Senegal 73.60% 63.80% 66.50% 69.10% 73.90% 72.50% 69.10% 65.10% 65.70% 69.80% 66.20% 62.40%

Serbia 2.00% -2.00% -9.20% -9.20% -9.60% 0.80% 3.20% 6.50% 9.70% 19.60% 19.40% 18.90%

Seychelles 12.10% 12.70% 3.80% 7.60% -2.30% -0.60% -1.40% 5.60% 12.40% 16.60% 14.50% 19.70%

Sierra Leone 71.70% 74.80% 76.50% 78.40% 77.00% 78.20% 79.10% 82.30% 86.20% 87.60% 85.80% 84.60%

Singapore -43.40% -31.00% -12.70% -23.80% -19.70% -13.50% -25.30% -32.20% -45.60% -45.70% -63.90% -76.10%

Slovakia -31.00% -48.60% -53.20% -28.00% -14.10% -1.10% 5.10% 11.00% 15.10% 16.50% 10.80% 10.30%

Slovenia -23.10% -25.10% -24.30% -9.10% -7.70% -1.10% 4.00% -0.40% 0.50% -2.80% -14.90% -30.10%

Solomon Islands 46.00% 47.40% 46.10% 48.00% 48.20% 49.50% 51.90% 63.60% 62.60% 60.30% 50.10% 46.90%

Somalia 66.00% 68.00% 69.20% 71.00% 71.60% 74.20% 74.90% 76.90% 80.80% 79.40% 77.60% 78.00%

South Africa 48.10% 47.70% 48.40% 52.10% 55.10% 51.00% 51.40% 49.60% 49.90% 62.70% 72.60% 74.60%

South America 28.70% 30.00% 29.60% 31.30% 29.50% 32.00% 28.40% 25.90% 24.50% 23.80% 15.20% 13.60%

South Sudan 77.90% 74.60% 72.70% 75.60% 77.30% 79.90% 81.00% 81.30% 80.40% 80.00% 77.30% 76.90%

South-Central Asia 60.80% 60.60% 59.20% 59.00% 58.20% 57.80% 56.90% 56.00% 55.50% 54.80% 48.00% 46.20%

South-Eastern Asia 40.80% 40.40% 40.60% 39.50% 39.60% 44.50% 38.80% 36.50% 32.80% 31.30% 32.00% 31.10%

Southern Africa 39.70% 41.10% 44.70% 50.70% 46.90% 47.00% 49.70% 48.70% 47.70% 60.90% 74.30% 76.00%

Southern Asia 61.50% 61.50% 60.10% 60.00% 59.00% 58.50% 57.50% 56.60% 55.90% 55.00% 48.10% 46.20%

Southern Europe -25.70% -27.10% -31.80% -26.20% -28.00% -20.20% -24.30% -27.70% -25.60% -29.40% -43.10% -51.80%


Country 1950-1955

1955-1960

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

SUSTAINABLE ENERGY -28.40% -35.10% -44.90% -39.70% -40.30% -35.90% -46.60% -45.30% -43.60% -45.00% -61.40% -77.40%

Sri Lanka 2.20% 10.00% 5.50% 11.80% 17.20% 13.50% 10.30% 13.00% 16.60% 30.40% 5.00% 8.90%

State of Palestine 44.30% 42.70% 34.20% 47.90% 41.90% 48.40% 46.80% 27.00% 25.10% 21.30% 11.40% 12.70%

Sub-Saharan Africa 62.90% 65.10% 66.20% 68.40% 69.00% 71.70% 72.50% 74.20% 76.80% 78.70% 77.40% 76.30%

Sudan 47.70% 49.80% 51.30% 54.30% 55.30% 60.80% 63.50% 65.90% 68.00% 69.40% 65.90% 64.60%

Suriname 17.50% 22.20% 13.60% 10.20% 12.50% 10.10% 21.90% 23.70% 28.60% 35.40% 33.60% 31.60%

Swaziland 55.00% 57.80% 60.30% 63.50% 62.60% 64.00% 61.60% 60.50% 61.60% 73.50% 79.90% 80.20%

Sweden -79.10% -72.00% -68.40% -55.90% -52.90% -37.20% -40.50% -42.90% -46.20% -51.50% -66.90% -77.50%

Switzerland -54.80% -51.00% -50.90% -43.10% -45.90% -40.00% -42.90% -50.00% -50.50% -57.70% -79.20% -94.20%

Syrian Arab Republic 39.90% 39.10% 38.20% 37.80% 31.20% 28.00% 18.60% 11.00% 4.50% 2.60% -13.60% -16.30%

Tajikistan 34.00% 34.10% 31.20% 35.20% 37.80% 42.90% 46.90% 50.50% 56.40% 57.90% 49.50% 48.60%

TFYR Macedonia -0.40% -7.40% 4.70% 12.60% 18.20% -1.10% 5.50% 1.20% 8.70% 11.00% 6.70% 9.90%

Thailand 31.00% 33.00% 30.80% 33.40% 29.50% 31.60% 25.50% 8.70% 13.60% 21.70% 16.40% 11.70%

Timor-Leste 71.00% 74.50% 73.90% 83.70% 81.40% 73.40% 72.70% 77.70% 76.40% 70.80% 59.70% 55.30%

Togo 64.80% 64.40% 63.60% 64.40% 65.50% 66.50% 65.70% 66.20% 68.50% 74.10% 73.60% 74.90%

Tonga -9.30% -0.70% -1.90% -1.40% -1.60% 11.10% 11.50% 12.20% 18.20% 22.60% 18.80% 21.10%

Trinidad and Tobago 3.90% 0.70% -5.30% 0.50% 7.70% 15.70% 18.70% 22.00% 26.40% 32.20% 30.20% 33.00%

Tunisia 62.90% 64.50% 63.70% 60.30% 51.70% 46.30% 32.40% 23.70% 11.50% 3.70% -6.60% -3.70%

Turkey 57.70% 58.40% 56.60% 55.30% 52.50% 51.60% 46.50% 40.80% 35.90% 28.40% 11.40% 5.10%

Turkmenistan 36.50% 34.00% 34.70% 35.50% 37.30% 43.20% 44.60% 47.10% 50.50% 50.90% 48.80% 51.00%

Uganda 57.50% 59.30% 59.80% 61.10% 63.40% 69.10% 72.10% 75.70% 79.40% 81.40% 77.60% 75.30%

Ukraine -12.90% -28.20% -39.70% -27.70% -16.30% 5.40% 16.70% 14.80% 30.80% 41.90% 42.40% 44.40%

United Arab Emirates 51.50% 46.90% 41.40% 31.50% 15.50% 2.90% -16.50% -34.00% -44.30% -53.70% -92.90% - 112.20%

United Kingdom -44.20% -38.60% -34.70% -25.30% -20.70% -9.60% -12.40% -14.40% -17.80% -20.20% -37.40% -49.10%

United Republic of Tanzania 55.50% 59.10% 62.00% 65.00% 65.80% 68.30% 69.90% 72.40% 76.20% 78.50% 75.50% 71.60%

United States of America -54.70% -48.90% -41.90% -25.90% -23.20% -21.80% -24.00% -21.90% -20.40% -19.50% -27.80% -30.30% United States Virgin Islands 0.10% -7.60% -14.10% -9.60% -24.20% -22.30% -16.20% -20.40% -21.80% -26.10% -36.00% -33.20%

Uruguay -37.40% -31.00% -32.30% -18.70% -10.50% -1.80% -3.20% 0.00% 1.10% 0.60% -7.90% -10.40%

Uzbekistan 22.60% 21.60% 19.40% 20.80% 22.00% 29.30% 28.90% 32.30% 39.60% 41.40% 37.80% 41.30%

Vanuatu 54.60% 55.70% 55.10% 53.60% 51.50% 52.10% 47.60% 45.50% 43.60% 42.00% 30.10% 25.40%

Venezuela (Bolivarian Republic of) 18.70% 17.90% 14.50% 10.40% 3.80% 6.90% 5.80% 3.00% 5.40% 10.70% 5.30% 5.70%

Vietnam 24.10% 19.30% 13.60% 16.40% 39.30% 19.00% 16.00% 13.90% 11.20% 7.50% -0.80% 2.50%

Western Africa 70.00% 66.50% 68.90% 72.00% 72.30% 74.20% 74.10% 75.60% 77.50% 79.70% 78.40% 77.50%

Western Asia 52.90% 51.40% 49.00% 47.50% 43.90% 43.40% 40.00% 33.90% 30.60% 26.90% 14.90% 13.40%

Western Europe -40.70% -37.90% -37.50% -24.70% -22.00% -14.10% -18.50% -25.30% -28.60% -31.80% -47.30% -57.50%

Western Sahara 65.40% 68.60% 69.70% 71.30% 70.60% 70.10% 65.50% 60.80% 59.90% 57.50% 47.70% 43.40%

WORLD 46.90% 47.60% 49.30% 42.40% 39.30% 40.90% 40.60% 40.70% 42.10% 43.50% 40.60% 39.40%

Yemen 75.00% 75.40% 74.10% 73.70% 71.90% 72.10% 69.00% 66.40% 65.50% 64.70% 61.20% 61.50%

Zambia 53.20% 56.70% 58.60% 61.60% 61.60% 65.70% 70.00% 76.80% 82.00% 84.80% 82.50% 79.30%

Zimbabwe 39.10% 41.30% 43.40% 47.30% 48.90% 52.60% 49.50% 52.50% 65.00% 78.70% 81.20% 78.60%


ANNEX 4: DEFICIT OR EXCESS (-VE) GDP BY GLOBAL HEALTH EQUITY ECONOMIC THRESHOLDS

Country name 1961 1970 1980 1990 2000 2010 2013

Afghanistan 1709037942 2303288597 11474904885 116196446494 144825395937

Albania 2951139671 3759639869 1364966315 2125532906

Algeria 480626399 540648823 -20009176613 -21703892167 21671620494 11243662528 2273649881

American Samoa

Andorra -69686366 -405016862 -945183648 -975995989

Angola 5627168331 24437303965 8489948029 -5355590557

Antigua and Barbuda -29438490 -296395421 -600518120 -729665900 -742399154

Argentina -22763535386 -44710957939 -91194540381 -255315801075 -274845071937 -385304599953

Armenia 3192812941 5503559442 4528672851 5696919733

Aruba -1654433726 -1994979468

Australia -17046146063 -36729706481 -133107414264 -285157546658 -369038706879 -1039281015851 -1435257967012

Austria -5524709252 -12468237791 -72557374584 -152949083834 -172758378185 -338642760346 -369754775794

Azerbaijan 2148316718 14129027304 -10773466762 -22534856168

Bahamas, The -161067957 -476124380 -1093694142 -2771903232 -5609785648 -6210712508

Bahrain -2659926960 -3467317798 -7452316760 -19890336692 -25569492695

Bangladesh 8032000033 15399468671 76502765717 134967367149 271993063415 602817216939 718682995770

Barbados -34811225 -93723393 -575108234 -1624612239 -2475422099 -3129035069

Belarus -1704921973 11380810342 -11064560231 -20416191299

Belgium -10084114535 -22829261932 -113977972926 -187508469113 -207961479802 -420406963792 -447452707841

Belize -6083103 -8289113 -29423305 -124705544 -256623797 37373102 193963310

Benin 386214253 736015969 2858947630 5729041906 14392751145 37690074908 47632688725

Bermuda -77775684 -166067856 -550598978 -1501191577 -3331166757 -5441396129

Bhutan 337723701 523862025 921242094 1750475763 2201750959

Bolivia 252030687 534173207 1620101463 5577655972 12080514187 27608393549 27222770159

Bosnia and Herzegovina 3736985404 1118946166 2922118416

Botswana 102450738 158746465 83181435 -1662995634 -1556872424 -4583493049 -3836522808

Brazil 3673813492 -6991107342 -95400564362 -231880830665 -224047059526 -1234767318241 -1159973813383

Brunei Darussalam -131362035 -4707417177 -3125546346 -5201326634 -10505886527 -13847293682

Bulgaria -9876339282 -7322710185 6791154506 -13315319320 -13642431688

Burkina Faso 884079532 1610644080 5896391180 10444875082 25370702623 63098674445 80182091709

Burundi 513292789 1028991241 3812997267 7486427530 15218290152 40932553877 52349381160

Cabo Verde 203647660 234215941 527267986 604466115 815244990

Cambodia 837414368 1864529694 25809949338 55596853001 66763109013

Cameroon 722977882 1330746446 3503486673 7405519622 29107362611 73470467495 91312354219

Canada -36160343554 -79918073166 -245231143604 -549288733555 -665283677723 -1455847695269 -1636256602073

Caribbean small states -935245060 -1841301469 -6935405003 -8414159964 -16757228601 -27324603912 -29035481878

Cayman Islands

Chad 438061876 871539917 4142677015 7411407619 18625375981 43878350823 56082510921

Channel Islands -6081192690

Chile -2726817899 -5457436134 -14735769168 -11243690972 -42074855312 -137700505095 -181723034074

China 116966599886 209516830696 935976835944 1388308165483 1845208363481 293754854876 -1885048616133

Colombia -394678396 653457314 -2509445987 10932240800 -3710037328 -70913464329 -116309044884


Country name 1961 1970 1980 1990 2000 2010 2013

Comoros 236159695 384596959 1071628632 2634956671 3324986729

Congo, Dem. Rep. 856810649 2483101036 15834366635 44322149384 94085989133 268848269508 335206515333

Congo, Rep. 110583438 216161732 354506985 865338586 4315996031 7123670899 9992201348

Costa Rica -141926116 -314555399 -2138037669 -2670460264 -6473929945 -14570577431 -23220330844

Cote d'Ivoire 290134534 472792867 -695864446 7831118442 28468505677 65376089347 79181317701

Croatia -17433340327 -10848639894 -38318337622 -34494460561

Cuba -2487110123 -8633036778 -12347590831 -3715364311 -11834863947

Curacao

Cyprus -2114562256 -6212741762 -10386621839 -25645335196 -22629653595

Czech Republic -23244369528 -34086785325 -149756809134 -141437307827

Denmark -5770855483 -14777454720 -63833766626 -127934419720 -147211045615 -287136398706 -300194661262

Djibouti 454627328 1191332638 2752105286 3273803664

Dominica 27276435 -57276764 -156498325 -144049616 -114652857

Dominican Republic 209549648 178755252 50508942 4065060598 -3112948404 -4372641399 -4238867193

Ecuador 192198561 -646251521 -8810191912 324841467 11884389354 2285345348 -4744806927

Egypt, Arab Rep. 5686271889 28619950619 43482094489 59587770115 144393526124 172665276427

El Salvador 241254597 1771228969 3415369901 1229938819 7514569754 10097856571

Equatorial Guinea 40680062 202350510 462643093 203103763 -8347186276 -11471521922

Eritrea 8789669425 24596211776 30873199616

Estonia -2308179455 -12838206566 -17298875794

Ethiopia 61909136334 151064000815 375863245941 463033555947

Faeroe Islands -950270534

Fiji -14317381 -28398156 -473992633 -217268656 272415187 779031698 746304823

Finland -4796672228 -9528074683 -47526004824 -131217340825 -109316239110 -211751097415 -227367656787

France -56420071456 -127350884695 -626985895743 -1154363943462 -1179504595958 - 2262490747952 -2377161251262

French Polynesia -213389253 -1188164592 -2876230157 -2875596466

Gabon -40597962 -107073905 -3446468281 -4496822503 -2113657420 -7328524159 -10285022423

Gambia, The 112240176 452069600 1092431295 2179335850 6868106283 9106408917

Georgia -355348983 7593129982 9080061130 8132386558

Germany -180114637420 -829820129477 -1592348884872 -1688225438716 -2923936112878 -3197958078923

Ghana 429287358 947194193 7944149237 16601132332 40396051297 80719539643 92440275893

Greece -2898207798 -9295203412 -43131737877 -77730091233 -98100903212 -242326557539 -181939972356

Greenland -52451456 -418477419 -933497194 -932551443

Grenada 18463820 -73039240 -278295105 -284055608 -260251013

Guam

Guatemala -4092711 88173188 150858465 6017960799 7717804511 25392442097 30020690385

Guinea 6588769588 18087717604 45869540173 57450722009

Guinea-Bissau 172500403 827831006 1320176526 2707537792 6547070664 8376178911

Guyana -40598483 -2642664 287857106 718106557 1081925132 1398506377 1257121634

Haiti 17013855932 39424784462 47447799155

Honduras 165586965 267017319 1603967894 4490095138 7925673019 19621637687 25328852099

Hong Kong SAR, China -731815550 -2344419990 -23054892885 -68158131860 -155601772338 -195954537313 -235065978711

Hungary -1739592326 -9878826489 -17107055295 -21771416964 -80973802509 -76358537904

Iceland -208737681 -443698016 -3069673709 -5981132871 -8019436315 -11084880744 -12869630611

India 75737158091 140717034759 612049073358 1009233790702 2035826816432 3901236688053 4908159480733


Country name 1961 1970 1980 1990 2000 2010 2013

Indonesia 32303582498 88854292857 160206143801 338638548518 410661705173 485598824910

Iran, Islamic Rep. -64915294 -45438861683 -29383694238 57596122938 -76099509506 50757866178

Iraq 53673413 1251299719 -41800402437

Ireland -1375748116 -3229191582 -17443098245 -43017618141 -88158916493 -188169076808 -192915435633

Isle of Man -1378522221

Israel -2935935765 -4278293864 -17333292794 -45325992078 -109735130546 -196202269846 -247685686147

Italy -32098378752 -89456011950 -395106094559 -1050890375111 -966747014870 -1779541164403 -1747100727001

Jamaica -331459410 -717195404 -233042992 -917796788 -2766748551 -710609418 349481573

Japan -29565657540 -170686494507 -953050883419 -2913771002051 - 4425370835202 - 4902368223255 -4211520969166

Jordan -84791128 -1408321240 713662987 3105539100 1706277896 1320861431

Kazakhstan -1799139797 17585903738 -72104059673 -132093794466

Kenya 1315649625 2535852968 11392336209 27474445567 62709183836 157907770231 196237800353

Kiribati 1838936 34581936 80803789 132052843 304360910 385656836

Korea, Dem. Rep.

Korea, Rep. 4140592000 2450243566 -24078246060 -218849429353 -448317099463 -864596028718 -1032429139352

Kosovo 2248758301 2521689028 2924052228

Kuwait -2597590339 -27065903655 -15261060113 -33122924597 -106015041385

Kyrgyz Republic 4077077591 10438202016 20554377832 23765904153

Lao PDR 5660010343 11257584179 22577408677 25541866347

Latvia -3352646180 -2125943778 -14249889135 -20046781863

Lebanon 1317164766 -9461282694 -17811122546 -20145015273

Lesotho 182753835 310976915 1067917027 1911486994 3703343706 7171697224 9011147529

Liberia 104801757 199157400 1315828860 2848585469 6442201790 17123581746 21317313239

Libya -22355819373 -21419089100 -46648701679 -41852280528

Liechtenstein -82276510 -505030116 -1377316032 -2404117885

Lithuania -4821599597 -2998353594 -22298053468 -29913664293

Luxembourg -624242917 -1320438146 -5550280910 -12081598180 -19215823194 -49694504251 -57439807699

Macao SAR, China -2437120100 -5060656595 -25872125012 -48684084047

Macedonia, FYR -1382079090 1359898970 442804586 1197255541

Madagascar 618143700 1307279921 5989234002 14669128265 34076160044 89376751393 113426116547

Malawi 735138614 1375780542 5915347563 12643323201 25547654984 64459155734 84958296254

Malaysia -296985454 -263846028 -9071138232 -16025873967 -37332315318 -115967845888 -151408464706

Maldives 134521351 116774918 33131426 -618671212 -430270385

Mali 1743007432 5937312600 9822867289 22311268561 55653528530 71972175080

Malta -139396856 -887039098 -2002825559 -3038119155 -6235161439 -7348692009

Marshall Islands -5756477 14799564 80744182 110207345

Mauritania 115043238 213296095 1050463932 2092368958 5234375807 13267457338 16915521743

Mauritius -28639416 -1025714004 -1721532155 -3758272655 -4914139420

Mexico -4081970359 -16049644857 -113668997702 -130374084001 -433254273063 -503109935220 -598033277455

Micronesia, Fed. Sts. 900304 25740289 188015451 226099962

Moldova 2089413636 7485039771 10762367019 11349889428

Monaco -284435110 -1347456024 -2436057760 -2570550600 -5179555913

Mongolia 797163618 4642359714 6421842014 3867647737

Montenegro 489045890 -1229697461 -1060689403

Morocco 1179550837 1917645620 4028646170 12339761526 32576832697 57911951295 76250245640

Mozambique 10399407847 18347414990 39744408557 102243726013 124666131209


Country name 1961 1970 1980 1990 2000 2010 2013

Myanmar

Namibia -1273244523 -628233819 666486679 -1002821244 -98668214

Nepal 1916770037 3386253122 14552075224 24216792285 50392630005 108918727257 131331462365

Netherlands -10558754606 -30555848814 -164541942119 -271884501666 -346685199921 -699847320317 -709116589765

New Caledonia -319087107 -1018450529 -2271025117 -2168343057

New Zealand -5059858247 -5435137082 -19469770552 -39978469443 -42712399025 -123143436790 -161561548135

Nicaragua 217426346 104168457 1583662068 5352023785 7188751857 18152161273 21692584578

Niger 379024363 973303916 4182770783 9440265799 24693249321 68234009705 89266431821

Nigeria 7169458976 8102652698 20322517038 116246055422 249815899281 374047839819 418964470782

Northern Mariana Islands

Norway -4722132658 -11303976404 -59028571595 -111102802327 -157462780183 -398196323562 -485030774509

Oman 96822123 9953628 -4657808100 -8902174001 -14582640307 -45771901371 -60887369595

Other small states -205458099 -104169763 10888657410 7220509063 11711961638

Pacific island small states -259039851 225045076 1302013468 3838114996 4271222258

Pakistan 7715671411 11751605342 68044244119 130781873514 272763526348 628487181944 750349379157

Palau -53690011 -113635587 -102313128 -133695255

Panama -168520704 -455066598 -1527899097 -1490004125 -4256688102 -11700002040 -21709359513

Papua New Guinea 259797191 250104564 1141852545 3172673937 9445606413 22434191195 24382277339

Paraguay 365269861 -426053654 838404681 4701120142 10026145289 6910663301

Peru -245920030 -2381432734 -787125752 8462896646 11993666187 -12351752336 -37699650281

Philippines -406935294 6483924472 21910834219 50928976679 106158252900 235201098337 261146927471

Poland -5957674556 -79051344064 -292132852022 -308756742617

Portugal -1165699680 -4806910892 -21256698544 -62315497165 -92495089965 -179743380823 -163343697419

Puerto Rico -1259932180 -4034864206 -10759140541 -25166097575 -52516112669 -81066721513 -83545746567

Qatar -261569073 -7572473452 -6627836903 -16328756168 -116980998343 -190698646766

Romania -2628358182 16795059710 -70584836996 -81461699347

Russian Federation -288828560442 93672260206 -862405240058 -1319195726089

Rwanda 633561612 1161236668 4641198707 8541764684 18503115673 44797822522 56361647750

Samoa 138331495 181218776 292609495 337190350

San Marino -708897212

Sao Tome and Principe 259390451 628245744 735084661

Saudi Arabia -2879781228 -153018937279 -91862715271 -139882108225 -399979908508 -589057815179

Senegal -12933248 526809375 2883694371 5835628120 19092577835 47325731027 61434158681

Serbia 12035822045 -3063408742 -3701144413

Seychelles -776170 1285113 -73496902 -260966014 -419306714 -555661581 -784817868

Sierra Leone 224202761 496957644 2546726014 5565611334 9343264436 24185422644 28081853614

Singapore -334987684 -1156376443 -9310380250 -34215219957 -86126470718 -212798771840 -268684668435

Sint Maarten (Dutch part)

Slovak Republic -3569466366 -15734201390 -61991468741 -66432865896

Slovenia -14309808782 -15185041954 -37467479491 -35668269798

Small states -150205963 -697990289 -7390730335 -8159945017 -4566557723 -16265979853 -12978482040

Solomon Islands 95775704 176924943 558860029 1767937634 1944740047

Somalia 518152719 951274573 6380688617 8801873893

South Africa -3216120896 -9782531568 -48916606430 -57897079991 -26812938470 -128393955270 -63537132728


Country name 1961 1970 1980 1990 2000 2010 2013

South Sudan 29916005689 47413852864

Spain -6082400597 -27326481229 -182857464035 -460761466317 -483288540339 -1168125331331 -1105494740740

Sri Lanka 1109849219 2306894067 12888703470 18126490823 29715733597 46529586741 43809018026

St. Kitts and Nevis 427180 210166 1551789 -96423276 -306780346 -448631663 -449318717

St. Lucia 1900966 -184753172 -386122522 -426805153 -344541286

St. Martin (French part)

St. Vincent and the Grenadines 6715312 14825256 54962864 -32921924 -137354663 -172841017 -132897142

Sudan 1073635437 2215824015 10792096866 21128332708 56958838910 114564514547 139169027097

Suriname -23145977 -110192728 -375575169 237063301 232767505 -1925427981 -2308739462

Swaziland 47134808 51866958 150000635 211673106 1039660163 1660079029 2979425040

Sweden -14250748457 -33181311366 -125277385986 -235266067752 -225873392348 -419426231943 -506969632330

Switzerland -9342278784 -20679389018 -105284122443 -233697945110 -238724850230 -512696027065 -606585833926

Syrian Arab Republic 250754590 206104357 -2790611135 6834504228 20137907196

Tajikistan 5514708780 14051548944 29847288980 35967607003

Tanzania 34800432072 71525313312 185660845465 232667784161

Thailand 4076313433 6479511729 21974120037 1647695530 27557295381 -9942135306 -24143007278

Timor-Leste 1707519054 4027633922 4769582680

Togo 276472087 524232981 1984118400 4194672018 10432535532 26168540525 32600538397

Tonga 53409595 32743862 47520158 115148961 104453634

Trinidad and Tobago -366736986 -473858570 -4991135651 -3189502223 -5097772062 -14578649458 -17373557097

Tunisia 446765627 -1421248964 246093120 1580215086 5030225052 11861871240

Turkey -898546985 -4295815521 -18433765602 -67664411001 -114281562416 -395516593141 -414170187884

Turkmenistan 2407155382 7946284291 1312015048 -13456549889

Turks and Caicos Islands

Tuvalu 5018099 8963231 13899711 15380210

Uganda 1325281963 2215232829 13148723936 22653848489 52324676571 142109365620 182145336533

Ukraine -1677118492 87279873735 77014178099 69063444398

United Arab Emirates -42434845289 -47924114454 -97042168023 -248143878897

United Kingdom -63379126248 -104319288484 -477323554914 -931294480723 -1351664364421 - 2003474943687 -2174058705452

United States -492727284271 -1000470151093 -2601895785097 - 5595826944440 -9609530182953 - 13519024228688 - 15086995727375

Uruguay -901052107 -1103570270 -6818965410 -4517583280 -14818164669 -23191482174 -37246130839

Uzbekistan 18171684184 45660928296 93566277600 107071498829

Vanuatu 19197382 67037728 174078979 398680080 534884305

Venezuela, RB -6938023822 -9048094083 -49821034145 -16677813996 -58311709387 -258664990027 -273527164653

Vietnam 95023036345 153493965414 288559698162 314712925663

Virgin Islands (U.S.) -17100578 -195824998 -616550759 -1404866210

West Bank and Gaza 2846551564 9402217608

Yemen, Rep. 12479202323 32602809082 74171091591 96301536307

Zambia 117982193 -247047895 2821666146 8771860599 21095483171 45307602055 56396672191

Zimbabwe -119026476 30970286 1680960598 7300238268 23450863285 51389546981 63870595337


ANNEX 5: HOLISTIC HEALTH INDEX 2012


Happiness index

LYLs x exhausting

LYLs x hoarding


Afghanistan 42.39 4.76 25.46 Albania 62.06 5.27 43.19 Algeria 59.50 5.24 -0.42 40.16 Angola 40.31 4.21 0.00 23.36 Argentina 62.25 6.44 -1.06 52.44 Armenia 59.67 4.37 35.02 Australia 65.17 7.41 -8.37 70.85 -15.39 Austria 64.65 7.35 -3.12 47.56 12.44 Azerbaijan 56.65 4.22 -1.28 31.01 Bahrain 59.47 4.55 -9.12 19.18 6.53 Bangladesh 48.93 4.99 31.77 Belarus 60.39 5.53 -2.03 43.23 Belgium 64.65 6.85 -4.26 43.13 11.26 Belize 60.42 6.45 51.10 Benin 46.07 3.67 22.52 Bolivia 52.76 5.78 39.91 Bosnia and Herzegovina 61.26 4.67 -3.01 34.90 Botswana 55.87 3.55 -0.05 26.30 Brazil 58.70 6.84 53.94 Bulgaria 62.08 4.22 -1.85 33.09 Burkina Faso 43.46 4.04 23.47 Burundi 41.18 3.79 20.70 Cambodia 49.73 4.16 27.47 Cameroon 48.70 4.43 28.36 Canada 65.57 7.65 -6.99 51.38 7.83 Central African Republic 40.84 3.57 19.85 Chad 43.63 3.74 21.88 Chile 62.63 6.64 -0.96 54.54 China 61.14 4.65 -1.96 35.54 Colombia 60.09 6.41 51.70 Comoros 49.26 3.92 25.21 Congo 45.90 3.82 23.41 Costa Rica 64.65 7.27 61.48 Cote d'Ivoire 46.63 4.20 26.34 Croatia 61.27 5.60 -1.21 44.71 Cuba 63.44 5.42 -0.50 44.33 Cyprus 65.71 6.39 -2.52 24.62 27.79 Czech Republic 61.99 6.15 -4.49 46.57 Denmark 64.24 7.77 -3.28 57.54 5.01 Djibouti 51.19 5.01 33.57 Dominican Republic 60.03 4.74 37.18 Ecuador 60.48 5.84 46.64 Egypt 53.28 3.88 -0.06 27.15 El Salvador 58.36 6.74 53.32 Estonia 60.31 5.14 -6.04 36.15 Ethiopia 40.23 4.37 23.62 Finland 62.86 7.39 -5.12 44.15 12.78 France 65.06 6.80 -1.77 39.91 17.24



Happiness index

LYLs x exhausting

LYLs x hoarding


Georgia 59.88 4.10 33.13 Germany 64.24 6.72 -3.76 41.87 11.63 Ghana 50.42 4.61 30.17 Greece 65.44 5.84 -3.01 47.13 Guatemala 54.80 6.29 45.88 Guinea 43.44 4.04 22.83 Guyana 54.42 5.99 43.53 Haiti 45.59 3.77 22.40 Honduras 58.15 5.87 44.59 Hungary 59.79 4.73 -1.35 36.84 Iceland 64.24 6.89 -2.13 42.72 13.07 India 49.58 4.99 32.13 Indonesia 55.66 5.46 39.86 Iran, Islamic Republic of 56.81 4.77 -2.69 33.31 Iraq 59.12 5.02 -0.59 37.64 Ireland 64.46 7.26 -3.60 44.90 13.19 Israel 65.02 7.36 -3.92 31.49 27.03 Italy 65.44 6.35 -2.55 32.98 19.47 Jamaica 62.11 6.21 -0.08 49.65 Japan 68.33 6.06 -3.93 48.09 2.56 Jordan 60.23 5.70 -0.49 43.95 Kazakhstan 56.76 5.51 -6.18 36.51 Kenya 53.22 4.26 29.58 Kuwait 64.74 6.58 -15.18 59.63 -19.81 Kyrgyzstan 55.60 5.00 37.54 Lao People's Democratic Republic 48.20 5.04 32.38

Latvia 59.74 4.67 -0.59 37.44 Lebanon 60.54 5.18 -1.24 39.92 Liberia 41.16 4.20 23.05 Libyan Arab Jamahiriya 60.39 4.92 -3.86 34.57 Lithuania 60.92 5.07 -0.98 40.99 Luxembourg 64.17 7.10 -10.80 110.99 -61.23 Madagascar 47.39 4.64 29.00 Malawi 40.83 5.15 28.06 Malaysia 61.52 5.58 -2.73 42.42 Mali 40.99 3.76 20.11 Malta 65.40 5.77 -2.16 47.30 Mauritania 49.97 4.97 32.28 Mauritius 60.04 5.48 -0.37 43.96 Mexico 61.80 6.80 -0.69 55.24 Mongolia 52.50 4.59 -0.83 31.51 Morocco 55.71 4.38 31.89 Mozambique 41.63 4.65 25.99 Myanmar 48.54 5.32 34.29 Namibia 52.89 4.89 34.66 Nepal 49.50 3.81 24.74 Netherlands 65.28 7.50 -4.85 44.24 15.32 New Zealand 63.89 7.22 -2.71 38.27 20.06 Nicaragua 59.14 5.69 44.43 Niger 38.78 4.10 21.15



Happiness index

LYLs x exhausting

LYLs x hoarding


Nigeria 46.02 4.76 28.66 Norway 63.99 7.63 -5.29 101.01 -41.72 Pakistan 52.88 5.27 36.17 Panama 62.77 7.32 -0.07 60.24 Paraguay 61.44 5.84 46.53 Peru 57.73 5.61 42.53 Philippines 57.23 4.94 38.18 Poland 61.01 5.78 -3.14 44.50 Portugal 63.45 4.87 -1.38 39.78 Qatar 62.57 6.59 -20.98 100.98 -69.23 Romania 60.40 4.91 -0.72 38.87 Russian Federation 59.08 5.46 -4.75 39.42 Rwanda 42.16 4.03 22.33 Saudi Arabia 61.57 6.73 -7.75 22.39 23.73 Senegal 49.56 3.83 24.95 Serbia 62.20 4.46 -2.00 34.67 Sierra Leone 40.84 4.13 23.02 Singapore 65.30 6.53 -0.09 50.82 5.06 Slovakia 60.80 6.05 -2.24 47.33 Slovenia 62.49 6.08 -2.81 48.12 South Africa 55.15 4.65 -2.53 32.13 Spain 66.12 6.19 -1.91 25.76 26.67 Sri Lanka 61.10 4.18 34.21 Sudan 50.92 4.38 29.08 Sweden 65.51 7.50 -1.79 55.51 7.29 Switzerland 66.04 7.52 -1.43 86.05 -21.67 Syrian Arab Republic 59.64 4.07 -0.20 31.46 Tajikistan 54.22 4.38 31.63 Tanzania, United Republic of 47.42 3.23 20.17 Thailand 61.97 6.22 -1.02 50.30 Togo 48.66 2.81 17.91 Trinidad and Tobago 58.88 6.70 -17.62 34.59 Tunisia 61.35 4.69 37.74 Turkey 57.10 5.49 -0.86 41.01 Turkmenistan 53.89 6.57 -3.70 43.98 Uganda 41.89 4.19 23.56 Ukraine 60.22 5.06 -2.07 39.29 United Arab Emirates 61.37 7.20 40.25 16.76 United Kingdom 64.37 7.03 -3.06 37.60 18.97 United States 64.30 7.16 -8.40 52.75 0.01 Uruguay 62.89 6.06 50.91 Uzbekistan 57.22 5.10 -0.56 38.26 Venezuela 61.92 7.48 -2.34 58.88 Vietnam 58.75 5.77 45.46 Yemen 49.27 3.92 25.16 Zambia 42.49 5.26 29.76 Zimbabwe 52.20 4.85 33.31


ANNEX 6: LIST OF THE TABLES

Table 1: Differences between measuring inequality and inequity ..................................................................... 31Table 2: Life expectancy of 14 HFS countries .................................................................................................. 40Table 3: GDP pc of 14 HFS .............................................................................................................................. 41Table 4: Total number of annual average avoidable deaths by 5 year periods 1950-2010 .............................. 46Table 5: Proportion of deaths worldwide which are avoidable by GHE ............................................................ 49Table 6: Burden of global health inequity in life years lost due to premature death 1950-2010 ....................... 54Table 7: Differences of features between average and lower limits of best-feasible-sustainable standards for global health equity ........................................................................................................................ 77Table 8: Percentage of GDP for ethical redistribution for global health equity.................................................. 78Table 9: Donor darlings and donor orphans in ODA 2012 ................................................................................ 80Table 10: Features of ODA and of redistribution required for global health equity ............................................. 81Table 11: Differences of present international framework for reporting of National Health Equity with required one ........................................................................................................................................ 86Table 12: Effects of hoarding and exhausting pc on life years lost ..................................................................... 88Table 13: Countries with higher holistic health index .......................................................................................... 93


ANNEX 7: LIST OF FIGURES

Figure 1: Use of global health vs international health in a sample of published books ..................................... 19Figure 2: Principles and vectors of global health ............................................................................................... 19Figure 3: Evolution of world average life expectancy ......................................................................................... 21Figure 4: Difference in national life expectancy between extremes ................................................................... 22Figure 5: Distribution of national average life expectancy levels, 5 years intervals, 2012 ................................. 22Figure 6: Distribution of national average life expectancy levels, one-year intervals, 2012 ............................... 23

........................................................... 23Figure 8: Median-mean gap of LE over time ...................................................................................................... 23Figure 9: Difference of lowest and highest under-5 mortality rates over time .................................................... 24Figure 10: Gini curve distribution of national average life expectancies, 2012 .................................................... 24

............................ 25 .............................................................. 25

Figure 13: Correlation between democratic index and years of education, and life expectancy ......................... 27Figure 14: Correlation between carbon emissions per capita and life expectancy .............................................. 28Figure 15: Correlation between life expectancy and GDP pc ............................................................................... 28Figure 16: Evolution of the correlation between life expectancy and GDP per capita.......................................... 29Figure 17: Analysis of health inequalities of under-5 mortality rate in Who-Health equity monitor ...................... 30Figure 18: Avoidable deaths due to economic global health inequity (Ref. HICS) ............................................... 33Figure 19: Number of avoidable deaths (millions) by global economic inequity 1990-2012 (Ref. HICS) ............. 34Figure 20: Percentage of all deaths due to global health inequity deaths (Ref. HICs) ......................................... 34Figure 21: Number of avoidable deaths due to global economic inequity 1970-2012 (source: GBD 2010) ........ 35Figure 22: Percentage of deaths due to global economic inequity 1970-2010 .................................................... 35Figure 23: Map of countries in 2010 above the weighted world average of life expectancy ................................ 37Figure 24: Map of countries in 2010 weigh GDP lower than world weighted average ......................................... 37Figure 25: Countries with constant rate of CO2 emissions per capita below planetary boundary ....................... 37Figure 26: Number of countries according to categories of healthy, feasible and sustainable criteria, 1960-2012 ........................................................................................................................................... 38

equitable) standards, 2012 ................................................................................................................. 38Figure 28: Carbon emissions per capita according to GDP pc and thresholds for feasible and sustainable standards, 2012 ............................................................................................................... 38Figure 29: Constant (1960-2012) healthy-feasible-sustainable (HFS) models .................................................... 39Figure 30: Population of 14 HFS .......................................................................................................................... 39Figure 31: Evolution of 14 HFS 1960-2012 .......................................................................................................... 40Figure 32: Female LE of 14 HFS.......................................................................................................................... 41Figure 33: Male LE of 14 HFS .............................................................................................................................. 41Figure 34: Evolution of GDP pc of 14 HFS, 1960-2013 ....................................................................................... 42Figure 35: World distribution of CO2 emissions per capita, 2000-2010 ............................................................... 42Figure 36: Evolution of CO2 emissions in 14 HFS, 2000-2012 ............................................................................ 43Figure 37: Map of LE gap from HFS standards in 1960 ....................................................................................... 44Figure 38: Map of LE gap from HFS standards in 1990 ....................................................................................... 44Figure 39: Map of LE gap from HFS standards in 2012 ....................................................................................... 44


Figure 40: Evolution of avoidable deaths in the countries with higher numbers, 1950-2010 ............................... 47Figure 41: Distribution of avoidable deaths by GHIE by the countries with higher numbers, 2005-2010 ............ 47Figure 42: Distribution by age group and evolution in time of total number of avoidable deaths by total health inequity ..................................................................................................................................... 48Figure 43: Distribution by age group and sex and evolution in time of total number of avoidable deaths due to global health inequity ............................................................................................................... 48Figure 44: Proportion of deaths which are avoidable in <15 year old, 1950-1955 ............................................... 50Figure 45: Proportion of deaths which are avoidable in <15 year olds, 2005-2010 ............................................. 50Figure 46: Proportion of deaths which are avoidable in 15-65 year olds, 1950-1955 .......................................... 50Figure 47: Proportion of deaths which are avoidable in 15-65 year olds, 2005-2010 .......................................... 51Figure 48: Proportion of deaths which are avoidable in <65 year olds, 1950-1955 ............................................. 51Figure 49: Proportion of deaths which are avoidable in >65 year olds, 2005-2010 ............................................. 51Figure 50: Proportion of all deaths which are avoidable by GHE, 1950-1955 ..................................................... 52Figure 51: Proportion of all deaths which are avoidable by GHE, 2005-2010 ..................................................... 52Figure 52: Evolution and age distribution of the proportion of avoidable deaths ................................................. 53Figure 53: Sex and age distribution of the proportion of avoidable deaths 1950-1955 ........................................ 53Figure 54: Sex and age distribution of the proportion of avoidable deaths 2005-2010 ........................................ 54Figure 55: Evolution 1950-2010 and age distribution of the life years lost due to global health inequity ............. 55Figure 56: Distribution of LYL due to global health inequity, by sex, in 2005-2010 .............................................. 56Figure 57: HFS models and world´s demographic pyramids 2005-2010 ............................................................. 56Figure 58: Distribution of GDP pc by country averages, 2013 ............................................................................. 57Figure 59: World´s GDP pc by population frequency of national GDP pc ............................................................ 57

....................... 58Figure 61: Distribution of countries’ populations by GDP pc, 2013 ...................................................................... 58Figure 62: Distribution of countries’ total GDP according to GDP pc intervals, 2013 ........................................... 59Figure 63: Gini index of global GDP, 2012 ........................................................................................................... 59

................... 60 ........................................... 60

Figure 66: Normal equity distribution to enable best feasible and sustainable health for all, ref average HFS standards, 2012 .......................................................................................................................... 62Figure 67: Time evolution of minimum, mean and maximum thresholds of equity distribution of resources to enable global health equity and the universal right to health, 1961-2012 (years 1-53) .................. 62Figure 68: Countries with GDP pc below the mDTh, 1950-1955 ......................................................................... 63Figure 69: Countries with GDP pc below the mDTh, 2005-2010 ......................................................................... 63Figure 70: Distribution of GDP required to enable global health equity, by countries and year 1960-2013 ......... 64Figure 71: Distribution of GDP required to enable global health equity, by share of countries and by years, 1961- 2013 ............................................................................................................................... 64Figure 72: Population living in countries under the mDTh ................................................................................... 65Figure 73: Proportion of the population living in countries under the mDTh, 1961-2012 ..................................... 65Figure 74: Trend of HFS countries, the world´s average, India and China GDP pc ............................................. 66Figure 75: Avoidable deaths vs mDThH ............................................................................................................... 66Figure 76: GDP by countries by GDP vs mDTh ................................................................................................... 67Figure 77: Relation of population and GDP vs MDT ............................................................................................ 67Figure 78: Time distribution -1960-2013- of world´s GDP according to basic MDT and excess .......................... 68


Figure 79: Proportion of world´s GDP as basic for HFS health standards and excess ........................................ 68Figure 80: Trend and share of hoarding GDP ...................................................................................................... 69Figure 81: Population from hoarding countries .................................................................................................... 69Figure 82: Population by GHE economic thresholds ........................................................................................... 70Figure 83: World´s GDP by GHE economic thresholds ....................................................................................... 70Figure 84: Ratio of max/min GDP pc thresholds for GHE, 1961-2013 ................................................................. 71Figure 85: Ethical shift from present inequity model (16 m avoidable deaths), to equity model enabling the universal right to health ................................................................................................................. 71Figure 86: Shift from present inequity population and resource distributions to ethical equitable distribution to enable global health equity, 2013 ................................................................................. 72Figure 87: Shift form present global health inequity to global health equity, ref. life expectancy 2012 ................ 73Figure 88: Required shift from present world´s population distribution according to national average carbon emissions to a normal and sustainable distribution, 2012 .................................................. 73 Figure 89: Countries by hoarding, equity zone world GDP average and < minimum dignity threshold 1960 ...... 74Figure 90: Countries by hoarding, equity zone world GDP average and minimum threshold 1990 ..................... 74Figure 91: Countries by hoarding, equity zone world GDP average and minimum threshold 2012 ..................... 74Figure 92: Trend of GDP required for ethical redistribution by hoarding countries 1961-2012 ............................ 75Figure 93: Trend of the share of each hoarding country of the total redistribution required, 1961-2012 ............. 76Figure 94: Normal equity distribution to enable best feasible and sustainable health for all, referred to lower limit of best-feasible-sustainable standards ............................................................................. 76

1961-2013 ........................................................................................................................................... 79 ...... 79

GHE (y axis) ........................................................................................................................................ 80Figure 98: Correlation of ODA with GHE needs, 1970-2012 ................................................................................ 81Figure 99: Distribution of avoidable deaths among countries with GDP pc above the mdt.................................. 82Figure 100: Trend of avoidable deaths by GHE in countries with GDP pc above the mdt ..................................... 83Figure 101: Correlation between life expectancy and inequality adjusted life expectancy .................................... 84

.................................................. 84Figure 103: Global vs national net health inequities 2012 ..................................................................................... 85Figure 104: Global vs national share of health inequity 2007-2012 ....................................................................... 85Figure 105: Correlation between hoarding effect on life years and carbon emissions per capita, 2010 ................ 89Figure 106: Correlation between hoarding and life expectancy, 2010 ................................................................... 89Figure 107: Healthy life expectancy of women, 2012 ............................................................................................ 90Figure 108: Healthy life expectancy of men, 2012 ................................................................................................. 91Figure 109: Happiness index ................................................................................................................................. 91Figure 110: Life years lost of others, per capita and life, due to hoarding effect .................................................... 92Figure 111: Life years lost of others, per capita and life, due to exhausting effect ................................................ 92Figure 112: Holistic health index ............................................................................................................................ 93Figure 113: Potential healthy happy and holistic (collective) life value best performer and world´s average ........ 94Figure 114: Holistic health index in the standard best feasible and sustainable health models for GHE .............. 94Figure 115: Holistic health index in 10 countries with highest HDI vs best HHI and potential HHI ........................ 95


ANNEX 8: LIST OF ACRONYMS

ACRONYMS

AIDS Acquired Immunodeficiency Syndrome

AvD Avoidable deaths

Bn Billion

CO2 Carbon dioxide

CSDH Commission on Social Determinants for Health

DAC Development Aid Committee

DALY Disability Adjusted Life Years

DHS Demographic Health Survey

EU European Union

GBD Global Burden of Disease

GDP Gross Domestic Product

GDP pc Gross Domestic Product per capita

GHE Global Health Equity

GHiE Global Health Inequity

HDI Human Development Index

HFS Healthy-Feasible-Sustainable

ICESCSRs International Covenant on Economic Social and Cultural Rights

LE Life expectancy

LYL Life years lost

mDTh Minimum dignity threshold

MICS Multiple indicator cluster survey

mT Metric Tons

NHE National Health Equity

OECD Organization for Economic Co-operation and Development

Tn Trillion

UDHR Universal Declaration of Human Rights

UN United Nations

UNDP United Nations Development Programme

US United States of America

WB World Bank

WHO World Health Organization

HEALTH EQUITYTHE KEY FOR TRANSFORMATIONAL CHANGE

health equity, the key for transformational change

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